  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.
