No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Ruixuan Liu,
Fangzhao Wu,
Chuhan Wu
et al.
Abstract:Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model can be trained on any participating client. However, in real applications, the devices of clients are usually heterogeneous, and have different computing power. Although big models like BERT have achieved huge success in AI, it is difficult to apply them to heterogeneous FL with weak clients. The straightforward solutions like remov… Show more
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