9 citations · 31 across the 17 of their papers we have counts for
3 papers · 1 filter
Towards Attack-tolerant Federated Learning via Critical Parameter Analysis
Sungwon Han, Sungwon Park, Fangzhao Wu +4
Federated learning is used to train a shared model in a decentralized way without clients sharing private data with each other. Federated learning systems are susceptible to poison…
FedDefender: Client-Side Attack-Tolerant Federated Learning
Sungwon Park, Sungwon Han, Fangzhao Wu +4
Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning a…
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Sungwon Han, Seungeon Lee, Fangzhao Wu +5
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair…