#Federated Learning

Five entries on this site carry the Federated Learning tag: two papers, one post and two talks, dated between 2020 and 2024.

Papers

Catfedavg: Optimising communication-efficiency and classification accuracy in federated learning
Paper · 2020 · arXiv · cited by 4 We propose CatFedAvg, a novel approach to federated learning that optimizes both communication efficiency and classification accuracy.
Fed-Focal Loss for imbalanced data classification in Federated Learning
Paper · 2020 · FL-IJCAI'20 · cited by 93 Federated Learning has emerged as a promising paradigm for training machine learning models while preserving data privacy. However, handling class imbalance in federated settings remains challenging.

Tackling Data Imbalance in Federated Learning
Post · 2024 How Fed-Focal Loss addresses one of the most challenging problems in distributed machine learning: handling imbalanced data across federated clients.

Decentralized AI: Privacy, Fairness, and the Future of Machine Learning
Talk · 2024 · AI & Web3 Summit 2024 A keynote on the convergence of federated learning, blockchain, and decentralized systems for privacy-preserving AI
Fed-Focal Loss for Imbalanced Data Classification in Federated Learning
Talk · 2021 · International Workshop on Federated Learning for User Privacy and Data Confidentiality (FL-IJCAI'20) A presentation on applying Focal Loss to Federated Learning for handling imbalanced data classification