4 citations · 4 across the 9 of their papers we have counts for
3 papers · 1 filter
HarMoEny: Efficient Multi-GPU Inference of MoE Models
Zachary Doucet, Rishi Sharma, Martijn de Vos +3
Mixture-of-Experts (MoE) models offer computational efficiency during inference by activating only a subset of specialized experts for a given input. This enables efficient model s…
Practical Federated Learning without a Server
Akash Dhasade, Anne-Marie Kermarrec, Erick Lavoie +3
Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coo…
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments
Joost Verbraeken, Martijn de Vos, Johan Pouwelse
Federated learning (FL) is a privacy-friendly type of machine learning where devices locally train a model on their private data and typically communicate model updates with a serv…