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Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients

Mohamed Ads, Hesham ElSawy, Hossam S. Hassanein

TL;DR

This work tackles the challenge of wireless Federated Learning under heterogeneous client data, varying transmission qualities, and potential adversaries. It introduces a risk-aware accelerated FL framework that combines dynamic SINR-based participation with a trustworthiness model, weighting updates by uplink reliability and selectively including risky users in early rounds before refining with fully trusted clients. A novel Success Uploading Model links per-user contribution to the probability of successful uplink transmission, and an adaptive switching rule guided by a trust window $\mu$ balances convergence speed and accuracy. Numerical results on a CNN trained on a digit dataset show that this approach outperforms conservative and risk-agnostic baselines, offering improved convergence and accuracy in hostile wireless environments.

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.

Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients

TL;DR

This work tackles the challenge of wireless Federated Learning under heterogeneous client data, varying transmission qualities, and potential adversaries. It introduces a risk-aware accelerated FL framework that combines dynamic SINR-based participation with a trustworthiness model, weighting updates by uplink reliability and selectively including risky users in early rounds before refining with fully trusted clients. A novel Success Uploading Model links per-user contribution to the probability of successful uplink transmission, and an adaptive switching rule guided by a trust window balances convergence speed and accuracy. Numerical results on a CNN trained on a digit dataset show that this approach outperforms conservative and risk-agnostic baselines, offering improved convergence and accuracy in hostile wireless environments.

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.
Paper Structure (11 sections, 7 equations, 4 figures, 1 algorithm)

This paper contains 11 sections, 7 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Federated Learning Model
  4. Communication Model
  5. Trustworthiness Model
  6. Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients
  7. Success Uploading Model
  8. Dynamic ${\rm SINR}$ Thresholds and Trustworthiness Integration: Algorithm Details
  9. Numerical Results
  10. Conclusion
  11. Appendix

Figures (4)

  • Figure 1: System model showing a test BS implementing dynamic ${\rm SINR}$ based aggregations with 4-levels and five clients in an FL training process.
  • Figure 2: Global Loss vs Communication Rounds with mean = 0.95.
  • Figure 3: Global Loss vs Communication Rounds with mean = 0.85.
  • Figure 4: Global Loss vs Communication Rounds with mean = 0.75.