To Talk or to Work: Energy Efficient Federated Learning over Mobile Devices via the Weight Quantization and 5G Transmission Co-Design

Federated learning (FL) is a new paradigm for large-scale learning tasks across mobile devices. However, practical FL deployment over resource constrained mobile devices confronts multiple challenges. For example, it is not clear how to establish an effective wireless network architecture to support FL over mobile devices. Besides, as modern machine learning models are more and more complex, the local on-device training/intermediate model update in FL is becoming too power hungry/radio resource intensive for mobile devices to afford. To address those challenges, in this paper, we try to bridge another recent surging technology, 5G, with FL, and develop a wireless transmission and weight quantization co-design for energy efficient FL over heterogeneous 5G mobile devices. Briefly, the 5G featured high data rate helps to relieve the severe communication concern, and the multi-access edge computing (MEC) in 5G provides a perfect network architecture to support FL. Under MEC architecture, we develop flexible weight quantization schemes to facilitate the on-device local training over heterogeneous 5G mobile devices. Observed the fact that the energy consumption of local computing is comparable to that of the model updates via 5G transmissions, we formulate the energy efficient FL problem into a mixed-integer programming problem to elaborately determine the quantization strategies and allocate the wireless bandwidth for heterogeneous 5G mobile devices. The goal is to minimize the overall FL energy consumption (computing + 5G transmissions) over 5G mobile devices while guaranteeing learning performance and training latency. Generalized Benders Decomposition is applied to develop feasible solutions and extensive simulations are conducted to verify the effectiveness of the proposed scheme.

Leveraging Coupled BBR and Adaptive Packet Scheduling to Boost MPTCP

Quite a few algorithms have been proposed to optimize the transmission performance of Multipath TCP (MPTCP). However, existing MPTCP protocols are still far from satisfactory in lossy and ever-changing networks because of their loss-based congestion control and the difficulty of managing multiple subflows. Recently, a congestion-based congestion control, BBR, is proposed to promote TCP transmission performance through better use of bandwidth. Due to the superior performance of BBR, we try to boost MPTCP with it. For this propose, coupled congestion control should be redesigned for MPTCP, and a functional scheduler able to effectively make use of the characteristics of BBR must also be developed for better performance. In this paper, we first propose Coupled BBR as a coupled congestion control algorithm for MPTCP to achieve high throughput and stable sending rate in lossy network scenarios with guaranteed fairness with TCP BBR flows and balanced congestion. Then, to further improve the performance, we propose an Adaptively Redundant and Packet-by-Packet (AR&P) scheduler, which includes two scheduling methods to improve adaptability in highly dynamic network scenarios and keep in-order packet delivery in asymmetric networks. Based on Linux kernel implementation and experiments in both testbed and real network scenarios, we show that the proposed scheme not only provides higher throughput, but also improves robustness and reduces out-of-order packets in some harsh circumstances.