… in personalized cross-silofederatedlearning with non-IID … in personalized cross-silofederated learning with non-IID data. … challenging personalized cross-silofederatedlearning prob…
… , a system solution for cross-silo FL that substantially reduces the … in FATE, an industrial cross-silo FL framework. Evaluations … requirements posed by cross-silofederatedlearning. We …
… lack of realistic cross-silo datasets, we propose FLamby, an open source cross-silofederated … Our ambition is that FLamby becomes the reference benchmark for cross-silo FL, as LEAF […
… In this paper, we first propose a federated estimation method to accurately … training fair models in cross-silofederatedlearning. We develop FedFair, a well-designed federatedlearning …
C Huang, M Tang, Q Ma, J Huang… - IEEE Communications …, 2023 - ieeexplore.ieee.org
… In cross-silofederatedlearning (FL), companies or … success of cross-silo FL relies on client cooperation, effective communication, and sufficient resource contributions for model training. …
… in federatedlearning. In particular, we show that meanregularized multi-task learning (MR-MTL), a simple personalization framework, is a strong baseline for cross-silo FL: under …
M Tang, VWS Wong - IEEE INFOCOM 2021-IEEE Conference …, 2021 - ieeexplore.ieee.org
… Abstract—In cross-silofederatedlearning (FL), organizations cooperatively train a global model with their local data. The organizations, however, may be heterogeneous in terms of …
Q Li, B He, D Song - arXiv preprint arXiv:2010.01017, 2020 - arxiv.org
… • Based on the knowledge transfer approach, we propose a new federatedlearning … federatedlearning algorithm for the cross-silo setting. Our experiments show that FedKT can learn …
W Bao, H Wang, J Wu, J He - … on Machine Learning, 2023 - proceedings.mlr.press
… We focus on cross-silo FL, where clients are organizations with data that differ in their … Clustered FederatedLearning Similar to our algorithm, clustered federatedlearning partitions …