5 citations · 10 across the 10 of their papers we have counts for
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Boosting Asynchronous Decentralized Learning with Model Fragmentation
Sayan Biswas, Anne-Marie Kermarrec, Alexis Marouani +3
Decentralized learning (DL) is an emerging technique that allows nodes on the web to collaboratively train machine learning models without sharing raw data. Dealing with stragglers…
Fair Decentralized Learning
Sayan Biswas, Anne-Marie Kermarrec, Rishi Sharma +2
Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such…
Communication-Efficient Secure Aggregation in Decentralized Learning
Sayan Biswas, Anne-Marie Kermarrec, Rafael Pires +2
Decentralized learning (DL) enables participants to collaboratively train models without a central server, yet it faces significant scalability challenges that demand sparsificatio…
Noiseless Privacy-Preserving Decentralized Learning
Sayan Biswas, Mathieu Even, Anne-Marie Kermarrec +4
Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used…
Low-Cost Privacy-Preserving Decentralized Learning
Sayan Biswas, Davide Frey, Romaric Gaudel +5
Decentralized learning (DL) is an emerging paradigm of collaborative machine learning that enables nodes in a network to train models collectively without sharing their raw data or…