Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations

TL;DR

This paper introduces TT-HF, a semi-decentralized federated learning architecture combining device-to-server and device-to-device communications, with convergence analysis and adaptive control to improve accuracy and efficiency in heterogeneous wireless environments.

Contribution

The paper proposes TT-HF, a novel hybrid federated learning framework with convergence guarantees and adaptive algorithms for resource-efficient model training.

Findings

TT-HF outperforms existing methods in accuracy and energy efficiency.

The convergence bounds guide adaptive parameter tuning.

Robust against channel outages and suitable for non-convex loss functions.

Abstract

Federated learning has emerged as a popular technique for distributing machine learning (ML) model training across the wireless edge. In this paper, we propose two timescale hybrid federated learning (TT-HF), a semi-decentralized learning architecture that combines the conventional device-to-server communication paradigm for federated learning with device-to-device (D2D) communications for model training. In TT-HF, during each global aggregation interval, devices (i) perform multiple stochastic gradient descent iterations on their individual datasets, and (ii) aperiodically engage in consensus procedure of their model parameters through cooperative, distributed D2D communications within local clusters. With a new general definition of gradient diversity, we formally study the convergence behavior of TT-HF, resulting in new convergence bounds for distributed ML. We leverage our convergence bounds to develop an adaptive control algorithm that tunes the step size, D2D communication rounds, and global aggregation period of TT-HF over time to target a sublinear convergence rate of O(1/t) while minimizing network resource utilization. Our subsequent experiments demonstrate that TT-HF significantly outperforms the current art in federated learning in terms of model accuracy and/or network energy consumption in different scenarios where local device datasets exhibit statistical heterogeneity. Finally, our numerical evaluations demonstrate robustness against outages caused by fading channels, as well favorable performance with non-convex loss functions.