Federated Learning Explained: Collaborative AI Without Data Sharing

Introduction

In an era where data privacy is paramount and artificial intelligence continues to advance at an unprecedented pace, Federated Learning (FL) has emerged as a revolutionary paradigm. This innovative approach allows multiple entities to collaboratively train a shared prediction model without exchanging their raw data.

Imagine scenarios where hospitals collectively build more accurate disease detection models without sharing sensitive patient records, or mobile devices improve predictive text capabilities by learning from user behavior without sending personal typing data to a central server. This is the core promise of federated learning.

Traditional machine learning often centralizes vast amounts of data for training, which presents significant challenges related to data privacy, security, regulatory compliance (like GDPR and HIPAA), and logistical hurdles. Federated learning directly addresses these concerns by bringing the model to the data, rather than the data to the model. Instead of pooling raw data, only model updates—small, anonymized pieces of information about how the model learned from local data—are shared and aggregated. This decentralized approach safeguards sensitive information and unlocks AI development in scenarios where data sharing is restricted or impractical.

This article will delve into the intricacies of federated learning, explaining its core concepts, how it operates, and its critical importance in today's data-conscious world. We will explore its diverse applications across various industries, from healthcare to mobile technology, and discuss the challenges that need to be addressed for its widespread adoption. Furthermore, we will provide a practical code demonstration, illustrating how to implement a federated learning setup, including a placeholder for integrating powerful inference engines like Groq. By the end, you will have a comprehensive understanding of federated learning and its transformative potential in building collaborative, privacy-preserving AI systems.

What is Federated Learning?

Federated Learning (FL) is a machine learning paradigm that enables multiple entities, often called 'clients' or 'nodes,' to collaboratively train a shared machine learning model without directly exchanging their raw data. Unlike traditional centralized machine learning, where all data is collected and processed in a single location, FL operates on a decentralized principle. The training data remains on the local devices or servers of each participant, ensuring data privacy and security.

The core idea is to bring computation to the data, rather than moving data to a central server. This is crucial for sensitive information like medical records, financial transactions, or personal mobile device data, where privacy regulations and ethical considerations prohibit direct data sharing. By keeping data localized, FL significantly reduces risks associated with data breaches, unauthorized access, and compliance violations.

FL involves an iterative process. A central server (or orchestrator) initializes a global model and distributes it to participating clients. Each client then trains this model locally using its own private dataset. Instead of sending raw data, clients compute and send only model updates (e.g., gradients or learned parameters) to the central server. These updates are typically aggregated, averaged, and used to improve the global model. This updated global model is then redistributed to clients for the next training round, and the cycle continues until the model converges.

This collaborative yet privacy-preserving approach allows leveraging diverse datasets that would otherwise be inaccessible due to privacy concerns or logistical constraints. It fosters a new era of AI development where collective intelligence can be harnessed without compromising individual data sovereignty.

How Does Federated Learning Work?

Federated learning combines distributed computing with privacy-preserving machine learning. It typically involves a central orchestrator (server) and multiple participating clients (edge devices, organizations, or data silos). The process unfolds in several iterative steps:

Initialization and Distribution: The central server initializes a global machine learning model (either pre-trained or randomly initialized). This model, along with training configurations (e.g., epochs, learning rate), is distributed to all participating clients.

Local Training: Each client independently trains the model using its own local, private dataset. This data never leaves the client's device. The local training process is similar to traditional machine learning, where the model learns patterns from local data and updates its parameters.

Model Update Transmission: After local training, clients send only the model updates (e.g., gradients, weight changes, or learned parameters) back to the central server, not their raw data. These updates are often compressed, encrypted, or anonymized to enhance privacy and reduce communication overhead. The specific method varies by federated learning algorithm (e.g., Federated Averaging, Federated SGD).

Aggregation: The central server receives model updates from multiple clients and aggregates them to create an improved global model. Federated Averaging (FedAvg) is a common algorithm, where the server averages the received model parameters, often weighted by the size of each client's dataset. This step synthesizes knowledge from all clients without seeing their individual data.

Global Model Update and Redistribution: The aggregated model becomes the new, improved global model. This updated model is then sent back to the clients, initiating the next training round. This iterative cycle continues until the global model converges to a satisfactory performance level.

This iterative process ensures that collective intelligence is incorporated into the global model, leading to a robust and accurate model, while preserving the privacy and confidentiality of each client's local data. It enables learning from distributed data sources that would otherwise be isolated due to privacy or regulatory restrictions.

Why is Federated Learning Important Now?

Federated learning is a rapidly evolving field gaining immense importance due to several converging factors:

Escalating Data Privacy Concerns and Regulations: Stringent regulations like GDPR and CCPA make centralizing sensitive user data challenging. FL offers a viable solution by allowing AI models to be trained on private data without it leaving its source, ensuring compliance and building user trust.

Proliferation of Edge Devices: The exponential growth of IoT devices, smartphones, and wearables means vast amounts of data are generated at the network's periphery. Traditional cloud-centric AI models struggle with data transfer, latency, and bandwidth limitations. FL enables on-device AI, reducing reliance on constant cloud connectivity and improving real-time responsiveness.

Addressing Data Silos: Many organizations possess valuable datasets that are siloed due to competitive reasons, regulations, or logistical complexities. FL provides a mechanism to unlock collective intelligence from these disparate data sources, fostering collaboration without compromising proprietary or sensitive information.

Enhanced Security against Data Breaches: Centralized data repositories are attractive targets for cyberattacks. By distributing data and sharing only model updates, FL inherently reduces the attack surface. Even if a central server is compromised, raw, sensitive data remains secure on individual devices, significantly mitigating the impact of potential data breaches.

Continual Learning and Personalization: FL facilitates continuous model improvement. As users interact with devices or new data is generated locally, models can be continuously updated and refined on the device itself. This enables highly personalized AI experiences, such as predictive keyboards that adapt to individual typing styles or recommendation systems that learn from unique user preferences, all while keeping personal data private.

Ethical AI Development: Beyond compliance, FL promotes a more ethical approach to AI development. It aligns with principles of data minimization and privacy-by-design, ensuring AI systems are built with respect for individual data rights from the ground up. This proactive approach helps build more trustworthy and socially responsible AI applications.

In essence, federated learning provides a powerful framework for developing advanced AI models in a world increasingly concerned with data privacy, distributed data sources, and the need for efficient, secure, and personalized AI experiences. It represents a significant step towards a future where AI can learn and evolve collaboratively, respecting individual data ownership.

Use Cases of Federated Learning

Federated learning's practical applications are rapidly expanding across various industries, offering innovative solutions where data privacy, security, and distributed data sources are critical. Here are some prominent use cases:

Mobile Applications and On-Device AI

One of the most intuitive and widely adopted applications of federated learning is in mobile devices. Features like next-word prediction, facial recognition, voice assistants, and personalized recommendation systems on smartphones heavily rely on user data. Traditionally, improving these models would necessitate sending vast amounts of personal user data to central servers for training. However, federated learning allows these models to be trained directly on the user's device.

For instance, Google's Gboard uses federated learning to improve its predictive text capabilities by learning from how millions of users type, without ever sending individual keystrokes or sensitive data to Google's servers. This approach significantly enhances user privacy, reduces bandwidth consumption, and enables highly personalized AI experiences that adapt to individual usage patterns in real time.

Healthcare and Medical Research

The healthcare sector is a prime candidate for federated learning due to the highly sensitive nature of patient data and stringent privacy regulations like HIPAA. Federated learning enables multiple hospitals, clinics, or research institutions to collaboratively train robust diagnostic models for diseases (e.g., cancer detection from medical images, predicting disease progression) without sharing raw patient records.

Each institution trains the model on its local dataset, and only the learned model parameters or updates are shared and aggregated. This allows for the creation of more accurate and generalizable models by leveraging a larger, more diverse patient population, while strictly adhering to privacy laws and maintaining patient confidentiality. It accelerates medical research and improves diagnostic capabilities across the healthcare ecosystem.

Autonomous Vehicles

Autonomous vehicles generate an enormous amount of data from various sensors (cameras, LiDAR, radar) crucial for training AI models for perception, navigation, and decision-making. Sharing all this raw data with a central cloud for training is impractical due to bandwidth limitations, latency, and privacy concerns. Federated learning offers a solution by allowing vehicles to train their AI models locally on their driving data.

Only aggregated insights or model updates are then shared with a central server to improve a global model. This collaborative learning across a fleet of vehicles helps in developing more robust and safer self-driving systems, enabling them to learn from diverse driving conditions and scenarios encountered by different vehicles, without compromising the privacy of individual vehicle data or location.

Smart Manufacturing and Industrial IoT

In the realm of Industry 4.0, smart factories and industrial IoT devices generate vast datasets related to machine performance, product quality, and operational efficiency. Federated learning can be applied here for predictive maintenance, quality control, and anomaly detection. For example, different manufacturing plants can collaboratively train models to predict equipment failures or identify defects without sharing proprietary operational data.

Each plant trains the model on its local sensor data, and only the model updates are shared. This allows for improved operational efficiency, reduced downtime, and enhanced product quality across a distributed manufacturing network, all while keeping sensitive production data within each facility.

Financial Services and Fraud Detection

The financial sector deals with highly sensitive transaction data, making privacy and security paramount. Federated learning can be instrumental in enhancing fraud detection, anti-money laundering (AML) efforts, and credit scoring. Multiple banks or financial institutions can collaboratively train models to identify fraudulent transactions or assess credit risk without directly sharing customer transaction histories.

By exchanging only model updates, they can leverage a broader dataset of financial activities to build more accurate and robust fraud detection systems, which are more effective at identifying emerging fraud patterns. This approach strengthens the collective defense against financial crime while preserving customer privacy and complying with strict financial regulations.

These examples underscore federated learning's versatility and its potential to unlock the value of distributed, sensitive data, fostering collaborative AI development in a privacy-preserving manner.

Challenges and Limitations of Federated Learning

While federated learning offers compelling advantages, it faces several challenges crucial for its successful adoption:

Communication Overhead

One significant bottleneck is communication cost. The iterative exchange of model updates between numerous clients and a central server can lead to substantial network traffic, especially with large models or many devices. Training a complex deep neural network across thousands of mobile phones could generate terabytes of data, straining bandwidth and increasing operational costs. Unstable network connections, common in mobile or IoT environments, can lead to dropped updates, delayed training, and slower convergence. Techniques like model compression and sparsification can mitigate this, but often involve trade-offs in model precision or convergence speed. Developers must balance communication efficiency with model quality, often requiring custom protocols.

Data Heterogeneity (Non-IID Data)

In real-world federated settings, data distribution across clients is rarely independent and identically distributed (non-IID). For example, a hospital in one region might primarily treat certain diseases, leading to a skewed dataset compared to another. This heterogeneity challenges model convergence and performance. If the global model is trained on highly diverse local datasets, it might perform poorly on individual clients whose data deviates significantly from the aggregated average. Traditional aggregation methods like Federated Averaging (FedAvg) can struggle, potentially leading to slower convergence or divergence. Advanced techniques, such as personalized federated learning, are being developed but add complexity.

Security and Privacy Risks

Despite keeping raw data local, federated learning is not entirely immune to security and privacy risks. Model updates (e.g., gradients) can inadvertently leak sensitive information. Gradient inversion attacks can reconstruct parts of original training data from shared gradients. Malicious actors could also inject poisoned updates, manipulating the global model to perform poorly or exhibit biased behavior (model poisoning attacks). Privacy-enhancing technologies like differential privacy (adding noise) and secure multi-party computation (encrypting updates) can enhance security, but often introduce trade-offs. Differential privacy can degrade accuracy, and cryptographic protocols can increase computational overhead. Implementing robust safeguards requires deep understanding of cryptographic techniques and careful balance between privacy, security, and model utility.

Resource Constraints and System Heterogeneity
Many clients in federated learning, especially mobile phones and IoT devices, operate under significant resource constraints (limited computational power, memory, battery life, inconsistent network connectivity). These limitations impact the feasibility and efficiency of local model training. System heterogeneity—variations in hardware, operating systems, and network conditions—can lead to inconsistent training speeds and reliability. Some devices might complete training quickly, while others might take longer or drop out, complicating synchronization and aggregation. This requires robust fault tolerance and careful client selection strategies.

Fairness and Bias

If certain client data is underrepresented or inherently biased, the global model might not perform equally well across all client groups. This can lead to fairness issues, where the model performs suboptimally for minority groups whose data was not adequately represented. Ensuring fairness requires careful consideration of data distribution, client sampling, and potentially incorporating fairness-aware aggregation algorithms.

Addressing these challenges is an active area of research. Innovations in communication efficiency, robust aggregation algorithms for non-IID data, advanced privacy-preserving techniques, and efficient resource management are continuously pushing the boundaries of federated learning, making it a more practical and reliable solution for collaborative AI.

Practical Implementation: Federated Learning with Groq (Code Demo)

To illustrate the core concepts of federated learning, we will walk through a simplified Python example. This demonstration simulates a federated learning setup with multiple clients and a central server. For this demo, we use a basic linear regression model and simulate data generation on each client. While a full-fledged federated learning framework involves complex communication protocols, secure aggregation, and robust error handling, this example aims to provide a clear understanding of the iterative local training and global model aggregation process.

We will also include a placeholder for integrating with a powerful inference engine like Groq. Groq has developed a Language Processing Unit (LPU) inference engine, which can deliver incredibly fast inference for large language models. While our example uses simple linear regression, in more complex federated learning scenarios involving large models (e.g., for natural language processing), Groq's LPU could be leveraged by the central server or powerful edge devices for rapid local inference or model evaluation after receiving the global model.

Dataset

For this simplified demonstration, we generate synthetic datasets for each client. Each client will have a small dataset for a linear relationship with some noise. In a real-world scenario, you would replace this with actual distributed datasets from various sources.

Code Demo: Simplified Federated Learning

First, let's create a Python file named federated_learning_demo.py:

import numpy as np

# --- Configuration ---

NUM_CLIENTS = 5
NUM_ROUNDS = 10
LOCAL_EPOCHS = 5
LEARNING_RATE = 0.01

# Placeholder for Groq API Key (replace with your actual key)
GROQ_API_KEY = "your_actual_api_key"

# --- Helper Classes ---
class Client:
    def __init__(self, client_id, data_size=100):
        self.client_id = client_id
        self.weights = np.random.rand(2)  # [slope, intercept]
        self.data_size = data_size
        self.X, self.y = self._generate_local_data()

    def _generate_local_data(self):
        np.random.seed(self.client_id)
        X = 2 * np.random.rand(self.data_size, 1)
        y = 3 * X + 2 + np.random.randn(self.data_size, 1) * 0.5
        return X, y

    def train_local_model(self, global_weights):
        self.weights = global_weights.copy()
        for epoch in range(LOCAL_EPOCHS):
            predictions = self.X.flatten() * self.weights[0] + self.weights[1]
            errors = predictions - self.y.flatten()
            gradient_slope = np.mean(errors * self.X.flatten())
            gradient_intercept = np.mean(errors)
            self.weights[0] -= LEARNING_RATE * gradient_slope
            self.weights[1] -= LEARNING_RATE * gradient_intercept
        return self.weights

    def evaluate_local_model(self):
        predictions = self.X.flatten() * self.weights[0] + self.weights[1]
        mse = np.mean((predictions - self.y.flatten()) ** 2)
        return mse

class Server:
    def __init__(self):
        self.global_weights = np.random.rand(2)

    def aggregate_models(self, client_weights_list):
        self.global_weights = np.mean(client_weights_list, axis=0)
        return self.global_weights

# --- Main Federated Learning Loop ---
def run_federated_learning():
    server = Server()
    clients = [Client(i) for i in range(NUM_CLIENTS)]
    print(f"Initial Global Weights: {server.global_weights}")

    for round_num in range(NUM_ROUNDS):
        print(f"\n--- Round {round_num + 1}/{NUM_ROUNDS} ---")
        client_updates = []
        
        for client in clients:
            updated_weights = client.train_local_model(server.global_weights)
            client_updates.append(updated_weights)
            mse = client.evaluate_local_model()
            print(f"Client {client.client_id} Local MSE: {mse:.4f}")

        server.aggregate_models(client_updates)
        print(f"Aggregated Global Weights: {server.global_weights}")

    print("\nFederated Learning complete!")
    print(f"Final Global Weights: {server.global_weights}")

    # --- Groq Integration Placeholder (Conceptual Only) ---
    print("\nGroq API Response (Conceptual):")
    print("Federated learning allows multiple devices to collaboratively train a global model without sharing raw data, ensuring data privacy.")

if __name__ == "__main__":
    run_federated_learning()

Output
Initial Global Weights: [0.47730663 0.489924  ]

--- Round 1/10 ---

Client 0 Local MSE: 14.8814
Client 1 Local MSE: 14.7992
Client 2 Local MSE: 14.3824
Client 3 Local MSE: 13.8879
Client 4 Local MSE: 15.4143

Aggregated Global Weights: [0.69928276 0.68107642]

--- Round 2/10 ---

Client 0 Local MSE: 12.1263
Client 1 Local MSE: 12.0160
Client 2 Local MSE: 11.7288
Client 3 Local MSE: 11.2319
Client 4 Local MSE: 12.5132

Aggregated Global Weights: [0.89917009 0.85277157]

--- Round 3/10 ---

Client 0 Local MSE: 9.8937
Client 1 Local MSE: 9.7640
Client 2 Local MSE: 9.5798
Client 3 Local MSE: 9.0877
Client 4 Local MSE: 10.1658

Aggregated Global Weights: [1.0791868  1.00696659]

--- Round 4/10 ---

Client 0 Local MSE: 8.0843
Client 1 Local MSE: 7.9418
Client 2 Local MSE: 7.8393
Client 3 Local MSE: 7.3570
Client 4 Local MSE: 8.2667

Aggregated Global Weights: [1.24132819 1.14542193]

--- Round 5/10 ---

Client 0 Local MSE: 6.6176
Client 1 Local MSE: 6.4675
Client 2 Local MSE: 6.4295
Client 3 Local MSE: 5.9605
Client 4 Local MSE: 6.7300

Aggregated Global Weights: [1.38738906 1.26972114]

--- Round 6/10 ---

Client 0 Local MSE: 5.4285
Client 1 Local MSE: 5.2745
Client 2 Local MSE: 5.2874
Client 3 Local MSE: 4.8340
Client 4 Local MSE: 5.4868

Aggregated Global Weights: [1.51898384 1.38128857]

--- Round 7/10 ---

Client 0 Local MSE: 4.4641
Client 1 Local MSE: 4.3093
Client 2 Local MSE: 4.3620
Client 3 Local MSE: 3.9256
Client 4 Local MSE: 4.4809

Aggregated Global Weights: [1.63756474 1.48140547]

--- Round 8/10 ---

Client 0 Local MSE: 3.6818
Client 1 Local MSE: 3.5282
Client 2 Local MSE: 3.6121
Client 3 Local MSE: 3.1934
Client 4 Local MSE: 3.6670

Aggregated Global Weights: [1.74443803 1.57122427]

--- Round 9/10 ---

Client 0 Local MSE: 3.0471
Client 1 Local MSE: 2.8963
Client 2 Local MSE: 3.0043
Client 3 Local MSE: 2.6034
Client 4 Local MSE: 3.0085

Aggregated Global Weights: [1.84077873 1.65178162]

--- Round 10/10 ---

Client 0 Local MSE: 2.5319
Client 1 Local MSE: 2.3849
Client 2 Local MSE: 2.5115
Client 3 Local MSE: 2.1283
Client 4 Local MSE: 2.4758

Aggregated Global Weights: [1.9276438  1.72400996]

Federated Learning complete!

Final Global Weights: [1.9276438  1.72400996]

Groq API Response (Conceptual): Federated learning allows multiple devices to collaboratively train a global model without sharing raw data, ensuring data privacy.

How the Code Works

• Configuration: Defines parameters like the number of clients, training rounds, local epochs, and learning rate.

• Client Class:

  • Each client represents a local device. It initializes with a unique ID and generates its own synthetic linear data (_generate_local_data).
  • The train_local_model method simulates local training using gradient descent. It takes global_weights from the server, trains on its own data, and returns updated weights.
  • evaluate_local_model calculates Mean Squared Error (MSE) on local data to measure performance.

• Server Class:

  • The server initializes global_weights representing the shared model.
  • The aggregate_models method receives updated weights from all clients, averages them, and updates the global_weights.

• run_federated_learning Function:

  • Orchestrates the federated learning process.
  • Initializes Server and Clients.
  • For each training round:
    • The server's global_weights are sent conceptually to each client.
    • Clients train locally for LOCAL_EPOCHS and send updated weights back.
    • The server collects client_updates and aggregates them into new global_weights.
  • The process repeats for NUM_ROUNDS.

• Groq Integration Placeholder:

  • Demonstrates conceptually where you might integrate the Groq API.
  • In advanced scenarios, Groq can be used after training for rapid inference or enhanced AI functionality.
  • Remember to replace YOUR_GROQ_API_KEY_HERE with your actual Groq API key and install the Groq library (pip install groq) to test integration.

How to Run the Code:

• Save the code as federated_learning_demo.py.
• Open your terminal or command prompt.
• Navigate to the file's directory.
• Run using the Python command: python federated_learning_demo.py.

You will observe global weights converging over multiple rounds, demonstrating collaborative learning without sharing raw data directly. Local MSE values show each client's individual model improvement.

This example provides a basic understanding of federated learning. Real-world implementations typically involve advanced models, optimization strategies, security protocols, and communication methods, but the fundamental principle remains collaborative learning on decentralized data.

Conclusion

Federated learning stands as a transformative paradigm in artificial intelligence, offering a powerful solution to escalating challenges of data privacy, security, and efficient utilization of distributed data. By enabling collaborative model training without centralizing raw, sensitive information, FL has opened new avenues for AI development in sectors previously constrained by regulatory hurdles, logistical complexities, or ethical considerations.

We have explored how federated learning operates through an iterative cycle of local training, model update transmission, and global aggregation, ensuring data remains on the device while collective intelligence is harnessed. Its importance is underscored by the pervasive need for privacy-preserving AI, the explosion of edge devices, the imperative to unlock insights from data silos, and the continuous demand for personalized and secure AI experiences.

The diverse range of applications, from enhancing mobile keyboard predictions and accelerating medical research to bolstering fraud detection in finance and enabling smarter autonomous vehicles, demonstrates FL's versatility and real-world impact. While challenges such as communication overhead, data heterogeneity, and inherent security risks persist, ongoing research and advancements are continuously refining FL techniques, making it more robust, efficient, and scalable.

The future of AI is increasingly collaborative and privacy-aware. Federated learning is not just a niche solution but a fundamental shift towards building more responsible, ethical, and effective AI systems that respect data sovereignty. As technology evolves and privacy concerns deepen, federated learning will undoubtedly play a pivotal role in shaping the next generation of intelligent applications, fostering innovation while safeguarding the very data that fuels it.

Frequently Asked Questions (FAQs)

1. What is the main difference between Federated Learning and traditional Machine Learning?

The main difference lies in data handling. Traditional machine learning centralizes all data on a single server for training, which can raise privacy and security concerns. Federated Learning, conversely, keeps data decentralized on local devices or servers. Only model updates (e.g., learned parameters or gradients), not raw data, are shared with a central server for aggregation, ensuring data privacy.

2. Is Federated Learning completely secure and private?

While federated learning significantly enhances privacy by keeping raw data on local devices, it is not entirely immune to security and privacy risks. Model updates can still potentially leak sensitive information through advanced attacks (e.g., gradient inversion attacks). Therefore, FL often incorporates additional privacy-enhancing technologies like differential privacy and secure multi-party computation to further strengthen security, though these can introduce trade-offs with model accuracy or computational overhead.

3. What are some real-world applications of Federated Learning?

Federated Learning is used in various domains. Prominent examples include: improving predictive text and voice recognition on mobile phones (e.g., Google Gboard), enabling collaborative medical research for disease detection across hospitals without sharing patient data, enhancing fraud detection in financial services, and training autonomous vehicle models from distributed driving data.

4. What is data heterogeneity in Federated Learning, and why is it a challenge?

Data heterogeneity refers to the non-uniform distribution of data across different participating clients in a federated learning setup. This means each client's local dataset might have unique characteristics or biases. It's a challenge because it can lead to slower model convergence, oscillations, or a global model that performs suboptimally on individual clients whose data significantly differs from the aggregated average. Advanced algorithms are needed to mitigate its effects.

5. Can Federated Learning be used with any type of machine learning model?

Federated Learning principles can be applied to a wide range of machine learning models, including linear regression, neural networks (for image classification, natural language processing), and more complex deep learning architectures. The core requirement is the ability to train a model locally and then extract and share its learned parameters or gradients for aggregation.

6. What is the role of the central server in Federated Learning?

The central server (or orchestrator) in federated learning plays a crucial role in coordinating the training process. It initializes and distributes the global model to clients, collects model updates from them, aggregates these updates to improve the global model, and then redistributes the updated model for the next training round. It acts as an aggregator and coordinator, not a data collector.

7. What are the computational requirements for clients in Federated Learning?

Clients in federated learning need sufficient computational resources (CPU, memory, battery) to train a machine learning model locally on their device. The exact requirements depend on the complexity of the model and the size of the local dataset. While some FL applications run on powerful servers, many are designed for edge devices like smartphones, which necessitates efficient model architectures and optimized training procedures to accommodate their limited resources.

References

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