动态选客户端+个性化，大幅降低联邦学习通信开销
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
- 按需求动态选设备参与训练，减少通信轮次
- 相比现有方法通信量降低95%，仍能高效收敛
- 适合数据分布不均的边缘设备联邦学习场景
联邦学习（FL）是一种分布式协同训练机器学习模型的方法，但其高通信开销带来通信瓶颈与网络扩展难题。本文提出ACSP-FL（https://github.com/AllanMSouza/ACSP-FL），通过动态调整参与训练的客户端数量和训练轮数，显著降低通信与计算成本。同时支持模型个性化，提升客户端性能。基于人体活动识别数据集的实验表明，与当前先进方法相比，ACSP-FL在非独立同分布（non-IID）数据场景下仍能高效收敛，通信开销最多降低95%。
原文摘要 · Abstract (English)
Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. FL requires a high level of communication between the devices and a central server, thus imposing several challenges, including communication bottlenecks and network scalability. This article introduces ACSP-FL (https://github.com/AllanMSouza/ACSP-FL), a solution to reduce the overall communication and computation costs for training a model in FL environments. ACSP-FL employs a client selection strategy that dynamically adapts the number of devices training the model and the number of rounds required to achieve convergence. Moreover, ACSP-FL enables model personalization to improve clients performance. A use case based on human activity recognition datasets aims to show the impact and benefits of ACSP-FL when compared to state-of-the-art approaches. Experimental evaluations show that ACSP-FL minimizes the overall communication and computation overheads to train a model and converges the system efficiently. In particular, ACSP-FL reduces communication up to 95% compared to literature approaches while providing good convergence even in scenarios where data is distributed differently, non-independent and identical way between client devices.
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