Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective (2004.04314v1)

Abstract: This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data. In such wireless federated learning networks (WFLNs), optimizing the learning performance depends crucially on how clients are selected and how bandwidth is allocated among the selected clients in every learning round, as both radio and client energy resources are limited. While existing works have made some attempts to allocate the limited wireless resources to optimize FL, they focus on the problem in individual learning rounds, overlooking an inherent yet critical feature of federated learning. This paper brings a new long-term perspective to resource allocation in WFLNs, realizing that learning rounds are not only temporally interdependent but also have varying significance towards the final learning outcome. To this end, we first design data-driven experiments to show that different temporal client selection patterns lead to considerably different learning performance. With the obtained insights, we formulate a stochastic optimization problem for joint client selection and bandwidth allocation under long-term client energy constraints, and develop a new algorithm that utilizes only currently available wireless channel information but can achieve long-term performance guarantee. Further experiments show that our algorithm results in the desired temporal client selection pattern, is adaptive to changing network environments and far outperforms benchmarks that ignore the long-term effect of FL.

Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: An Analytical Perspective

The paper "Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective" offers a sophisticated approach to optimizing federated learning (FL) models deployed over wireless networks. The authors articulate a novel framework addressing the intertwined challenges of client selection and bandwidth allocation within the scope of wireless federated learning networks (WFLNs). By concentrating on a long-term optimization horizon, this paper advances the discussion beyond the conventional short-sighted strategies prevalent in existing literature.

Summary of Contributions

The primary focus of the paper is to optimize client selection and bandwidth allocation in WFLNs by adopting a long-term perspective instead of assessing these metrics on a per-round basis. The authors address several key areas:

1. Temporal Client Selection Patterns: The authors illuminate the significant influence of temporal client selection patterns on FL performance. Through empirical analysis on tasks such as image classification and text generation, they establish that allocating more client resources in later learning rounds—termed the "Ascend" pattern—results in higher accuracy and robustness relative to other patterns.
2. Formulation of a Stochastic Optimization Problem: The core of the paper is the formulation of a stochastic optimization problem for joint client selection and bandwidth allocation under long-term client energy constraints. This formulation accounts for the temporal importance of each learning round, introducing a time-dependent weight to guide resource allocation.
3. The OCEAN Algorithm: To solve the formulated problem, the paper introduces a novel online optimization algorithm—named OCEAN—that utilizes currently available wireless channel information to provide long-term performance guarantees. The optimality of the algorithm is underscored by deriving a significant $[O(1/V), O(\sqrt{V})]$ tradeoff between learning performance and energy consumption.
4. Benchmarking and Empirical Validation: The proposed algorithm is thoroughly benchmarked against existing methodologies, showcasing its adaptability to varying network conditions and superior FL performance in terms of both learning speed and model accuracy.

Implications for Future Research

The implications of this research are multifaceted, both practical and theoretical:

• Practical Deployment in WFLNs: By accounting for long-term interdependencies and varying significance across learning rounds, the proposed approach supports more efficient deployment of FL in real-world WFLNs, potentially leading to enhanced model performance even in resource-constrained environments.
• Energy Allocation in Heterogeneous Networks: The flexibility of the OCEAN algorithm in adapting to different network environments suggests its potential applicability to more heterogeneous networks where devices vary widely in capability and battery life.
• Exploration of Optimal Selection Patterns: While the paper identifies an ascending selection trend as favorable, the exploration of other potentially optimal patterns remains an open area. Given the intricacies involved in diverse learning scenarios, further research could unveil novel patterns tailored to specific applications.

Conclusion

This paper makes notable strides in the domain of FL over wireless networks by recognizing the importance of long-term resource allocation strategies. The introduction of a stochastic optimization framework and the OCEAN algorithm represent tangible advances suitable for practitioners aiming to deploy robust, efficient FL models. As the field continues to evolve, building on these insights could facilitate the development of more sophisticated adaptive systems that leverage the full potential of decentralized learning in wireless settings. Future exploration into client heterogeneity and optimal selection patterns promises to extend these contributions even further, potentially reshaping how FL systems are engineered and deployed across various domains.