Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning (2505.03533v1)

Abstract: Judicious resource allocation can effectively enhance federated learning (FL) training performance in wireless networks by addressing both system and statistical heterogeneity. However, existing strategies typically rely on block fading assumptions, which overlooks rapid channel fluctuations within each round of FL gradient uploading, leading to a degradation in FL training performance. Therefore, this paper proposes a small-scale-fading-aware resource allocation strategy using a multi-agent reinforcement learning (MARL) framework. Specifically, we establish a one-step convergence bound of the FL algorithm and formulate the resource allocation problem as a decentralized partially observable Markov decision process (Dec-POMDP), which is subsequently solved using the QMIX algorithm. In our framework, each client serves as an agent that dynamically determines spectrum and power allocations within each coherence time slot, based on local observations and a reward derived from the convergence analysis. The MARL setting reduces the dimensionality of the action space and facilitates decentralized decision-making, enhancing the scalability and practicality of the solution. Experimental results demonstrate that our QMIX-based resource allocation strategy significantly outperforms baseline methods across various degrees of statistical heterogeneity. Additionally, ablation studies validate the critical importance of incorporating small-scale fading dynamics, highlighting its role in optimizing FL performance.

Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning

The paper presents a sophisticated approach to resource allocation for federated learning (FL) in wireless networks, focusing specifically on adapting to rapid channel fluctuations within rounds of FL gradient uploading—a factor often neglected by existing strategies due to reliance on block fading assumptions. This work proposes a small-scale-fading-aware resource allocation strategy using a multi-agent reinforcement learning (MARL) framework, delivering significant benefits to FL performance in environments characterized by system and statistical heterogeneity.

Key Contributions

• Decentralized Resource Allocation with MARL: The paper introduces a decentralized resource allocation strategy modeled as a decentralized partially observable Markov decision process (Dec-POMDP), employing the QMIX algorithm to optimize decision-making. In this MARL framework, each FL client acts as an agent, dynamically selecting spectrum and power allocations based on local observations and rewards derived from convergence analysis. This approach reduces the dimensionality of the action space, facilitating scalable decentralized decision-making.
• Convergence Analysis: The authors establish a one-step convergence bound for the FL algorithm that accounts for local gradient drift in non-IID settings, providing insights into the impact of each local gradient upload on overall FL convergence. This theoretical analysis guides the design of effective resource allocation strategies.
• Performance Evaluation: Experimental results demonstrate that the proposed method outperforms baseline resource allocation strategies, particularly in scenarios with varying degrees of statistical heterogeneity. The paper also includes ablation experiments underscoring the importance of incorporating small-scale fading dynamics, further validating the effectiveness of the approach.

Implications and Future Directions

The insights presented in this paper have substantial implications for improving the robustness of FL in wireless settings, particularly in high-dynamic environments. By explicitly incorporating small-scale fading dynamics into resource allocation decisions, the solution exemplifies a critical advancement in optimizing FL performance, thus contributing to the growing field of decentralized machine learning.

From a theoretical perspective, the convergence analysis deepens the understanding of FL dynamics, emphasizing the significance of addressing statistical heterogeneity in conjunction with system-level challenges. Practically, this methodology offers a viable strategy for deploying FL in resource-constrained networks, such as IoT and cellular systems, where communication latency is a primary bottleneck.

Looking ahead, future research could expand on this approach, potentially integrating client selection mechanisms to consider fluctuations in channel conditions alongside spectrum and power allocations. Additionally, further exploration into MARL frameworks might reveal enhanced strategies for multi-client collaboration, particularly in larger-scale FL scenarios.

In summary, this paper makes substantial strides in addressing the nuanced challenges of wireless FL, presenting a robust framework that enhances both theoretical understanding and practical applications by meticulously integrating small-scale fading conditions into resource allocation strategies.