Robustness of Decentralised Learning to Nodes and Data Disruption

arXiv:2405.02377v1 Announce Type: new
Abstract: In the vibrant landscape of AI research, decentralised learning is gaining momentum. Decentralised learning allows individual nodes to keep data locally where they are generated and to share knowledge extracted from local data among themselves through an interactive process of collaborative refinement. This paradigm supports scenarios where data cannot leave local nodes due to privacy or sovereignty reasons or real-time constraints imposing proximity of models to locations where inference has to be carried out. The …

abstract ai research arxiv collaborative cs.lg data data disruption decentralised disruption generated interactive knowledge landscape nodes paradigm process research robustness through type