AFAFed -- Protocol analysis

• URL: http://arxiv.org/abs/2206.14927v1
• Date: Wed, 29 Jun 2022 22:12:08 GMT
• Title: AFAFed -- Protocol analysis
• Authors: Enzo Baccarelli, Michele Scarpiniti, Alireza Momenzadeh and Sima Sarv Ahrabi
• Abstract summary: This is a novel A Fair Federated Adaptive learning framework for stream-oriented IoT application environments. We analyze the convergence properties and address the implementation aspects AFAFed.
• Score: 3.016628653955123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we design, analyze the convergence properties and address the implementation aspects of AFAFed. This is a novel Asynchronous Fair Adaptive Federated learning framework for stream-oriented IoT application environments, which are featured by time-varying operating conditions, heterogeneous resource-limited devices (i.e., coworkers), non-i.i.d. local training data and unreliable communication links. The key new of AFAFed is the synergic co-design of: (i) two sets of adaptively tuned tolerance thresholds and fairness coefficients at the coworkers and central server, respectively; and, (ii) a distributed adaptive mechanism, which allows each coworker to adaptively tune own communication rate. The convergence properties of AFAFed under (possibly) non-convex loss functions is guaranteed by a set of new analytical bounds, which formally unveil the impact on the resulting AFAFed convergence rate of a number of Federated Learning (FL) parameters, like, first and second moments of the per-coworker number of consecutive model updates, data skewness, communication packet-loss probability, and maximum/minimum values of the (adaptively tuned) mixing coefficient used for model aggregation.

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