A personalized federated cloud-edge collaboration framework via cross-client knowledge distillation

As an emerging distributed machine learning paradigm, federated learning has been extensively used in the domain of cloud-edge computing to collaboratively train models without uploading their raw data. However, the existing federated learning methods make an effort to train a single optimal model that encompasses all participating clients. These methods may perform poorly on some clients due to variations in data distribution and limited data availability of clients. Moreover, assigning weights to clients merely based on the quantity of the client data neglects the inter-client correlation. In this paper, we propose a personalized federated learning framework with cross-client knowledge distillation called FedCD. FedCD is composed of a local model training strategy with cross-client co-personalized knowledge fusion and a global model weighted aggregation mechanism via peer correlation. In the local model training strategy, FedCD fuses similar personalized knowledge from all clients to guide the lcoal training of the client. In the global model weighted aggregation mechanism, the server assigns weights to clients based on their influence among clients. Extensive experiments conducted on various datasets demonstrate that FedCD significantly improves the test accuracy by approximately 0.18%-16.65% compared to the baseline methods.