354 citations · 673 across the 11 of their papers we have counts for
11 papers
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Federated Learning of Large Models at the Edge via Principal Sub-Model Training
Yue Niu, Saurav Prakash, Souvik Kundu +2
Federated Learning (FL) is emerging as a popular, promising decentralized learning framework that enables collaborative training among clients, with no need to share private data b…
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
Romain Cosentino, Anirvan Sengupta, Salman Avestimehr +4
Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotatio…
Federated Learning for Internet of Things: Applications, Challenges, and Opportunities
Tuo Zhang, Lei Gao, Chaoyang He +3
Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. W…
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning
Sunwoo Lee, Tuo Zhang, Chaoyang He +1
In Federated Learning, a common approach for aggregating local models across clients is periodic averaging of the full model parameters. It is, however, known that different layers…
Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning
Jinhyun So, Ramy E. Ali, Basak Guler +2
Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conv…