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.
Victoria Dochkina
LLM agents spontaneously self-organize into specialized roles and hierarchies without pre-assignment, outperforming centralized coordination by 14%.
Ruicheng Ao, Siyang Gao, David Simchi-Levi
This paper establishes fundamental reliability limits of LLM-based multi-agent planning by modeling it as a delegated decision network and proving it is dominated by a centralized Bayes decision maker.
Jiangnan Fang, Cheng-Tse Liu, Jieun Kim, et al.
A multi-LLM framework for text summarization using centralized and decentralized evaluation strategies.
Micah Sheller, Brandon Edwards, G. Anthony Reina, et al.
Federated learning enables multi-institutional medical collaborations without sharing patient data, achieving 99% of centralized model quality across 10 institutions.
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.