Wireless Federated Learning (FL) is an emerging distributed machine learning paradigm, particularly gaining momentum in domains with confidential and private data on mobile clients. However, the location-dependent performance, in terms of transmission rates and susceptibility to transmission errors, poses major challenges for wireless FL's convergence speed and accuracy. The challenge is more acute for hostile environments without a metric that authenticates the data quality and security profile of the clients. In this context, this paper proposes a novel risk-aware accelerated FL framework that accounts for the clients heterogeneity in the amount of possessed data, transmission rates, transmission errors, and trustworthiness. Classifying clients according to their location-dependent performance and trustworthiness profiles, we propose a dynamic risk-aware global model aggregation scheme that allows clients to participate in descending order of their transmission rates and an ascending trustworthiness constraint. In particular, the transmission rate is the dominant participation criterion for initial rounds to accelerate the convergence speed. Our model then progressively relaxes the transmission rate restriction to explore more training data at cell-edge clients. The aggregation rounds incorporate a debiasing factor that accounts for transmission errors. Risk-awareness is enabled by a validation set, where the base station eliminates non-trustworthy clients at the fine-tuning stage. The proposed scheme is benchmarked against a conservative scheme (i.e., only allowing trustworthy devices) and an aggressive scheme (i.e., oblivious to the trust metric). The numerical results highlight the superiority of the proposed scheme in terms of accuracy and convergence speed when compared to both benchmarks.

翻译：无线联邦学习（FL）是一种新兴的分布式机器学习范式，在移动设备上处理机密和隐私数据的领域尤为活跃。然而，传输速率和传输错误易感性等位置依赖性能对无线FL的收敛速度与精度构成了重大挑战。对于缺乏验证数据质量与设备安全指标的恶劣环境，这一挑战更为严峻。为此，本文提出一种新型风险感知加速联邦学习框架，该框架综合考虑了客户端在数据量、传输速率、传输错误率及可信度方面的异构性。基于客户端的位置依赖性能与可信度特征对其进行分类，我们提出一种动态风险感知全局模型聚合方案：允许客户端按照传输速率降序和信任度升序约束参与聚合。具体而言，初始轮次以传输速率为主导参与准则以加速收敛，随后逐渐放宽传输速率限制，以探索小区边缘客户端上的更多训练数据。聚合轮次中引入去偏因子以修正传输错误的影响。通过验证集实现风险感知：基站在微调阶段剔除不可信客户端。将该方案与保守方案（仅允许可信设备参与）和激进方案（忽略信任度量）进行对比。数值结果表明，所提方案在精度与收敛速度上均优于两种基准方案。