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Prototypical Contrastive Federated Learning on Non-IID data

prototypical

Special Sessions > SS03 Advanced Sensing, Monitoring and Diagnosis of Renewable Energy batteries

Federated learning allows multiple clients to collaborate to

train high-performance deep learning models while keeping

the training data locally. However, when the local data of all

clients are not independent and identically distributed (i.e.,

non-IID), it is challenging to implement this form of efficient

collaborative learning. Although significant efforts have been

dedicated to addressing this challenge, the effect on the im-

age classification task is still not satisfactory. In this paper, we

propose FedProc: prototypical contrastive federated learning,

which is a simple and effective federated learning framework.

The key idea is to utilize the prototypes as global knowledge

to correct the local training of each client. We design a lo-

cal network architecture and a global prototypical contrastive

loss to regulate the training of local models, which makes

local objectives consistent with the global optima. Eventu-

ally, the converged global model obtains a good performance

on non-IID data. Experimental results show that, compared

to state-of-the-art federated learning methods, FedProc im-

proves the accuracy by 1.6% ∼ 7.9% with acceptable com-

putation cost.