Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge (1804.08333v2)

Abstract: We envision a mobile edge computing (MEC) framework for ML technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e. requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol.

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

The paper "Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge" addresses significant challenges in implementing Federated Learning (FL) within practical Mobile Edge Computing (MEC) frameworks. The core contribution of this work is the introduction of a protocol, FedCS, designed to efficiently manage federated learning processes while accounting for heterogeneous client resources. This research underscores the practical implications and the theoretical advancements in optimizing resource utilization within FL environments to enhance model training efficiency.

Key Contributions and Methodology

The FL protocol is traditionally hampered by inefficiencies arising from clients with varying computational power and differing wireless channel conditions. The random selection of clients in the traditional FL protocol leads to prolonged update and aggregation cycles, especially when clients with suboptimal resources are selected. This inefficiency is exacerbated in heterogeneous networks where resources and conditions fluctuate widely among different clients.

FedCS addresses this challenge through a novel client selection mechanism that optimally manages these heterogeneous resources. The protocol involves multiple steps:

1. Resource Request: The MEC operator randomly selects a fraction of clients and requests their resource information, including computational capacity, data size, and wireless channel conditions.
2. Client Selection: Leveraging the resource information, the MEC operator uses a greedy algorithm to select clients who can complete the FL tasks within a predefined deadline. This optimization problem is aimed at maximizing the number of client updates aggregated by the server.
3. Model Distribution and Update: The server distributes the global model to the selected clients. Clients update the model with their local data and upload the updated parameters back to the server within the scheduled time frame.

The paper evaluates the performance of FedCS using publicly-available large-scale image datasets (CIFAR-10 and Fashion-MNIST) and simulates a MEC environment under various configurations and conditions. The experimental results demonstrate that FedCS significantly reduces the training time compared to the original FL protocol, achieving faster convergence to the desired model performance.

Experimental Results

The experiments conducted in the paper show compelling results. For the CIFAR-10 dataset, FedCS achieved a 75\% accuracy much faster than the baseline FL protocol constrained by similar deadlines. On the Fashion-MNIST dataset, FedCS reached an 85\% accuracy nearly twice as fast as the baseline. These results were consistent regardless of the stochastic variations in throughput and computation capabilities.

The experiments also explored the sensitivity of the FedCS protocol to different deadline settings (T_round). The findings indicate that a moderately optimized deadline can balance the inclusion of more clients per round against the overall number of aggregation steps, thus enhancing the training efficiency. Too short or too long deadlines can detrimentally impact the training performance, highlighting the need for adaptive deadline settings based on real-time conditions.

Theoretical and Practical Implications

From a theoretical standpoint, this research contributes to the optimization algorithms used in federated learning by introducing a client selection strategy that can handle diverse client capabilities and network conditions. The integration of a greedy algorithm for selection decisions under specific bandwidth constraints enriches the FL literature with methods to maximize client contributions within a limited time frame.

Practically, FedCS has significant implications for deploying FL in real-world MEC environments. By reducing the training time and improving model convergence speed, FedCS makes a compelling case for enhancing the efficiency of privacy-preserving ML applications in sectors like autonomous vehicles, smart cities, and personalized health care, where timely and accurate model updates are crucial.

Future Developments

Building upon the findings, future research directions may include:

• Extending the FedCS protocol to dynamically adjust T_round based on continuous monitoring of client resources and network conditions.
• Exploring more sophisticated global models, such as deep neural networks with advanced architectures, to assess the scalability of FedCS.
• Investigating client selection algorithms that incorporate model compression techniques to further alleviate non-IID challenges and enhance communication efficiency.

Conclusion

The paper offers a significant advancement in federated learning within MEC frameworks. By addressing the client heterogeneity issue through a resource-aware client selection protocol, FedCS demonstrates marked improvements in training efficiency and model performance. This work stands as a testament to the evolving landscape of federated learning, providing a practical path towards more efficient, real-world implementations that preserve data privacy while maximizing computational resource utilization.