Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data

• URL: http://arxiv.org/abs/2106.14503v2
• Date: Wed, 30 Jun 2021 02:35:38 GMT
• Title: Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data
• Authors: Pravin Chandran, Raghavendra Bhat, Avinash Chakravarthi, Srikanth Chandar
• Abstract summary: Federated Learning allows training of data stored in distributed devices without the need for centralizing training data. We propose a novel Divide-and-Conquer training methodology that enables the use of the popular FedAvg aggregation algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning allows training of data stored in distributed devices without the need for centralizing training data, thereby maintaining data privacy. Addressing the ability to handle data heterogeneity (non-identical and independent distribution or non-IID) is a key enabler for the wider deployment of Federated Learning. In this paper, we propose a novel Divide-and-Conquer training methodology that enables the use of the popular FedAvg aggregation algorithm by overcoming the acknowledged FedAvg limitations in non-IID environments. We propose a novel use of Cosine-distance based Weight Divergence metric to determine the exact point where a Deep Learning network can be divided into class agnostic initial layers and class-specific deep layers for performing a Divide and Conquer training. We show that the methodology achieves trained model accuracy at par (and in certain cases exceeding) with numbers achieved by state-of-the-art Aggregation algorithms like FedProx, FedMA, etc. Also, we show that this methodology leads to compute and bandwidth optimizations under certain documented conditions.

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