LINDT: Tackling Negative Federated Learning with Local Adaptation

• URL: http://arxiv.org/abs/2011.11160v1
• Date: Mon, 23 Nov 2020 01:31:18 GMT
• Title: LINDT: Tackling Negative Federated Learning with Local Adaptation
• Authors: Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu
• Abstract summary: We propose a novel framework called LINDT for tackling NFL in run-time. We introduce a metric for detecting NFL from the server. Experiment results show that the proposed approach can significantly improve the performance of FL on local data.
• Score: 18.33409148798824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without disclosing each client's data. However, FL may fail to proceed properly, amid a state that we call negative federated learning (NFL). This paper addresses the problem of negative federated learning. We formulate a rigorous definition of NFL and analyze its essential cause. We propose a novel framework called LINDT for tackling NFL in run-time. The framework can potentially work with any neural-network-based FL systems for NFL detection and recovery. Specifically, we introduce a metric for detecting NFL from the server. On occasion of NFL recovery, the framework makes adaptation to the federated model on each client's local data by learning a Layer-wise Intertwined Dual-model. Experiment results show that the proposed approach can significantly improve the performance of FL on local data in various scenarios of NFL.

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