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Federated Learning (FL) is a paradigm that aims to support loosely connectedclients in learning a global model collaboratively with the help of acentralized server. The most popular FL algorithm is Federated Averaging(FedAvg), which is based on taking weighted average of the client models, withthe weights determined largely based on dataset sizes at the clients. In thispaper, we propose a new approach, termed Federated Node Selection (FedNS), forthe server's global model aggregation in the FL setting. FedNS filters andre-weights the clients' models at the node/kernel level, hence leading to apotentially better global model by fusing the best components of the clients.Using collaborative image classification as an example, we show withexperiments from multiple datasets and networks that FedNS can consistentlyachieve improved performance over FedAvg.

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