Over-The-Air Clustered Wireless Federated Learning

• URL: http://arxiv.org/abs/2211.03363v3
• Date: Tue, 17 Oct 2023 04:27:42 GMT
• Title: Over-The-Air Clustered Wireless Federated Learning
• Authors: Ayush Madhan-Sohini, Divin Dominic, Nazreen Shah, Ranjitha Prasad
• Abstract summary: Over-the-air (OTA) FL is preferred since the clients can transmit parameter updates simultaneously to a server. In the absence of a powerful server, decentralised strategy is employed where clients communicate with their neighbors to obtain a consensus ML model. We propose the OTA semi-decentralised clustered wireless FL (CWFL) and CWFL-Prox algorithms, which is communication efficient as compared to the decentralised FL strategy.
• Score: 2.2530496464901106
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Privacy and bandwidth constraints have led to the use of federated learning (FL) in wireless systems, where training a machine learning (ML) model is accomplished collaboratively without sharing raw data. While using bandwidth-constrained uplink wireless channels, over-the-air (OTA) FL is preferred since the clients can transmit parameter updates simultaneously to a server. A powerful server may not be available for parameter aggregation due to increased latency and server failures. In the absence of a powerful server, decentralised strategy is employed where clients communicate with their neighbors to obtain a consensus ML model while incurring huge communication cost. In this work, we propose the OTA semi-decentralised clustered wireless FL (CWFL) and CWFL-Prox algorithms, which is communication efficient as compared to the decentralised FL strategy, while the parameter updates converge to global minima as O(1/T) for each cluster. Using the MNIST and CIFAR10 datasets, we demonstrate the accuracy performance of CWFL is comparable to the central-server based COTAF and proximal constraint based methods, while beating single-client based ML model by vast margins in accuracy.

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