Federated Learning with Diverse Tasks and Data

Abstract	In this paper, we propose a method for distribu- tively selecting relevant data for a given federated learning task. We use a benchmark model trained on a small benchmark dataset that is task-specific, to evaluate the relevance of individual data samples at each client and select the data with sufficiently high relevance. The effectiveness of our approach is demonstrated experimentally.
Authors	Tiffany Tuor (Imperial) Shiqiang Wang (IBM US) Bongjun Ko (IBM US) Changchang Liu (IBM US) Kin Leung (Imperial)
Date	Sep-2019
Venue	Annual Fall Meeting of the DAIS ITA, 2019