62 citations · 376 across the 37 of their papers we have counts for
7 papers · 1 filter
Revisiting Ensembling in One-Shot Federated Learning
Youssef Allouah, Akash Dhasade, Rachid Guerraoui +5
Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a signi…
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…
Energy-Aware Decentralized Learning with Intermittent Model Training
Akash Dhasade, Paolo Dini, Elia Guerra +5
Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the i…
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
Akash Dhasade, Anne-Marie Kermarrec, Tuan-Anh Nguyen +2
Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual…
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…