Scheduling Policies for Federated Learning in Wireless Networks (1908.06287v2)

Abstract: Motivated by the increasing computational capacity of wireless user equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the increasing concerns about sharing private data, a new machine learning model has emerged, namely federated learning (FL), that allows a decoupling of data acquisition and computation at the central unit. Unlike centralized learning taking place in a data center, FL usually operates in a wireless edge network where the communication medium is resource-constrained and unreliable. Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration. Due to the shared nature of the wireless medium, transmissions are subjected to interference and are not guaranteed. The performance of FL system in such a setting is not well understood. In this paper, an analytical model is developed to characterize the performance of FL in wireless networks. Particularly, tractable expressions are derived for the convergence rate of FL in a wireless setting, accounting for effects from both scheduling schemes and inter-cell interference. Using the developed analysis, the effectiveness of three different scheduling policies, i.e., random scheduling (RS), round robin (RR), and proportional fair (PF), are compared in terms of FL convergence rate. It is shown that running FL with PF outperforms RS and RR if the network is operating under a high signal-to-interference-plus-noise ratio (SINR) threshold, while RR is more preferable when the SINR threshold is low. Moreover, the FL convergence rate decreases rapidly as the SINR threshold increases, thus confirming the importance of compression and quantization of the update parameters. The analysis also reveals a trade-off between the number of scheduled UEs and subchannel bandwidth under a fixed amount of available spectrum.

Scheduling Policies for Federated Learning in Wireless Networks

The paper "Scheduling Policies for Federated Learning in Wireless Networks" addresses the integration of federated learning (FL) within wireless network environments. The focus is on understanding how different scheduling policies affect FL convergence performance, given the constrained and often unreliable nature of wireless communication channels.

Background and Motivation

Federated learning presents a framework where data remains localized on edge devices, thereby reducing privacy concerns and data transmission load by only sharing model updates instead of raw data. The traditional centralized learning mechanisms are unsuitable for modern wireless networks where bandwidth and reliability are critical constraints. The paper aims to explore how FL can be efficiently implemented considering these constraints, particularly through the choice of scheduling policies for user equipment (UE) in a wireless setup.

Contributions and Approach

1. Algorithm Proposal: The researchers introduce an algorithm tailored for federated learning in wireless networks. This algorithm allows each UE to solve local subproblems, updating parameters by incorporating local data while maintaining alignment with global updates. The method summarizes an approach that effectively decouples computations from data transmission, allowing simultaneous data privacy and efficient model training.
2. Analytical Framework: An analytical model is established to predict the performance dynamics of FL under varying scheduling conditions and inter-cell interference levels. This approach provides closed-form expressions for FL convergence rates, offering insights into the interaction between scheduling policies and network performance.
3. Scheduling Policies: The paper evaluates three scheduling policies: Random Scheduling (RS), Round Robin (RR), and Proportional Fair (PF). For each policy, detailed analyses reveal which conditions favor their effectiveness. Notably, PF outperforms RS and RR in high SINR (Signal-to-Interference-plus-Noise Ratio) settings, while RR is more effective under low SINR conditions.
4. Numerical Insights: The analysis highlights the impact of system parameters such as the SINR threshold and the number of subchannels. The results underscore the importance of SINR management and parameter quantization in enhancing FL performance.
5. Theoretical Implications: The research indicates that efficient scheduling can substantially mitigate the challenges posed by user interference and unreliable channels. It asserts the necessity for intelligent scheduling to meet the demands of next-generation wireless networks propelled by edge computing capabilities.

Implications and Future Directions

The paper significantly contributes to the theoretical understanding of FL in wireless networks by combining stochastic geometry with optimization theory. The implications extend to practical scheduling mechanisms in intelligent networks, where spectrum efficiency and computational load balancing are critical.

Future research could further optimize these scheduling policies by incorporating emerging wireless technologies such as massive MIMO and full-duplex operations. Additionally, enhancing the robustness of FL algorithms against model heterogeneity and non-i.i.d. data remains a crucial area for continued investigation, potentially fueled by advances in machine learning algorithms and network protocol designs.

Conclusion

In summary, this paper provides a comprehensive analysis and novel solutions for deploying efficient federated learning across wireless networks. The insights into scheduling policies and their impact on FL convergence offer a foundational step towards integrating decentralized learning processes with modern telecommunications infrastructures. This work stands as a pivotal reference for researchers and practitioners aiming to harness the capabilities of federated learning within varied wireless contexts.