An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

Understand

The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public.

• A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally, without the necessity to upload their personal sensitive data.
• In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process.
• The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.