RC SSFL: Towards Robust and Communication efficient Semi supervised Federated Learning System
Federated Learning (FL) is an emerging decentralized artificial intelligence paradigm, which promises to train a shared global model in high quality while protecting user data privacy. However, the current systems rely heavily on a strong assumption: all clients have a wealth of ground truth labeled data, which may not be always feasible in the real life. In this paper, we present a practical Robust, and Communication efficient Semi supervised FL (RC SSFL) system design that can enable the clients to jointly learn a high quality model that is comparable to typical FL's performance. In this setting, we assume that the client has only unlabeled data and the server has a limited amount of labeled data. Besides, we consider malicious clients can launch poisoning attacks to harm the performance of the global model. To solve this issue, RC SSFL employs a minimax optimization based client selection strategy to select the clients who hold high quality updates and uses geometric median aggregation to robustly aggregate model updates. Furthermore, RC SSFL implements a novel symmetric quantization method to greatly improve communication efficiency. Extensive case studies on two real world datasets demonstrate that RC SSFL can maintain the performance comparable to typical FL in the presence of poisoning attacks and reduce communication overhead by $2 \times \sim 4 \times
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