FedNS: Improving Federated Learning for collaborative image classification on mobile clients

• URL: http://arxiv.org/abs/2101.07995v1
• Date: Wed, 20 Jan 2021 06:45:46 GMT
• Title: FedNS: Improving Federated Learning for collaborative image classification on mobile clients
• Authors: Yaoxin Zhuo, Baoxin Li
• Abstract summary: Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model. We propose a new approach, termed Federated Node Selection (FedNS), for the server's global model aggregation in the FL setting. We show with experiments from multiple datasets and networks that FedNS can consistently achieve improved performance over FedAvg.
• Score: 22.980223900446997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model collaboratively with the help of a centralized server. The most popular FL algorithm is Federated Averaging (FedAvg), which is based on taking weighted average of the client models, with the weights determined largely based on dataset sizes at the clients. In this paper, we propose a new approach, termed Federated Node Selection (FedNS), for the server's global model aggregation in the FL setting. FedNS filters and re-weights the clients' models at the node/kernel level, hence leading to a potentially better global model by fusing the best components of the clients. Using collaborative image classification as an example, we show with experiments from multiple datasets and networks that FedNS can consistently achieve improved performance over FedAvg.

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