Fed-Focal Loss for imbalanced data classification in Federated Learning

• URL: http://arxiv.org/abs/2011.06283v1
• Date: Thu, 12 Nov 2020 09:52:14 GMT
• Title: Fed-Focal Loss for imbalanced data classification in Federated Learning
• Authors: Dipankar Sarkar, Ankur Narang, Sumit Rai
• Abstract summary: Federated Learning has a central server coordinating the training of a model on a network of devices. One of the challenges is variable training performance when the dataset has a class imbalance. We propose to address the class imbalance by reshaping cross-entropy loss such that it down-weights the loss assigned to well-classified examples along the lines of focal loss.
• Score: 2.2172881631608456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Federated Learning setting has a central server coordinating the training of a model on a network of devices. One of the challenges is variable training performance when the dataset has a class imbalance. In this paper, we address this by introducing a new loss function called Fed-Focal Loss. We propose to address the class imbalance by reshaping cross-entropy loss such that it down-weights the loss assigned to well-classified examples along the lines of focal loss. Additionally, by leveraging a tunable sampling framework, we take into account selective client model contributions on the central server to further focus the detector during training and hence improve its robustness. Using a detailed experimental analysis with the VIRTUAL (Variational Federated Multi-Task Learning) approach, we demonstrate consistently superior performance in both the balanced and unbalanced scenarios for MNIST, FEMNIST, VSN and HAR benchmarks. We obtain a more than 9% (absolute percentage) improvement in the unbalanced MNIST benchmark. We further show that our technique can be adopted across multiple Federated Learning algorithms to get improvements.

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