5 papers · 1 filter
Robust Federated Inference
Akash Dhasade, Sadegh Farhadkhani, Rachid Guerraoui +4
Federated inference, in the form of one-shot federated learning, edge ensembles, or federated ensembles, has emerged as an attractive solution to combine predictions from multiple…
Mosaic Learning: A Framework for Decentralized Learning with Model Fragmentation
Sayan Biswas, Davide Frey, Romaric Gaudel +7
Decentralized learning (DL) enables collaborative machine learning (ML) without a central server, making it suitable for settings where training data cannot be centrally hosted. We…
Adaptive Gradient Clipping for Robust Federated Learning
Youssef Allouah, Rachid Guerraoui, Nirupam Gupta +3
Robust federated learning aims to maintain reliable performance despite the presence of adversarial or misbehaving workers. While state-of-the-art (SOTA) robust distributed gradien…
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…
Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
Youssef Allouah, Abdellah El Mrini, Rachid Guerraoui +2
Federated learning (FL) is an appealing paradigm that allows a group of machines (a.k.a. clients) to learn collectively while keeping their data local. However, due to the heteroge…