Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients

• URL: http://arxiv.org/abs/2401.09267v1
• Date: Wed, 17 Jan 2024 15:15:52 GMT
• Title: Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients
• Authors: Mohamed Ads, Hesham ElSawy and Hossam S. Hassanein
• Abstract summary: Wireless Federated Learning (FL) is an emerging distributed machine learning paradigm. This paper proposes a novel risk-aware accelerated FL framework that accounts for the clients heterogeneity in the amount of possessed data. 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)
• Score: 21.104752782245257
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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