A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper reviews federated learning applications, highlighting its role as a decentralized privacy-preserving technology to address data silos and data sensitivity challenges.
Jiangnan Fang, Cheng-Tse Liu, Jieun Kim, et al.
A multi-LLM framework for text summarization using centralized and decentralized evaluation strategies.
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, et al.
This paper surveys the unique challenges, methods, and future directions of federated learning, a paradigm for training models across decentralized data sources.
Peter Kairouz, H. Brendan McMahan, Brendan Avent, et al.
A comprehensive survey of federated learning advances and open problems, emphasizing decentralized training, privacy, and systems challenges.