5 papers · 1 filter
Efficient and Robust Online Learning to Rank in Decentralized Systems
Marcel Gregoriadis, Martijn de Vos, Sayan Biswas +2
In Online Learning to Rank (OLTR), ranking models are trained directly from live user interactions, but existing systems rely on a trusted central server to collect and process the…
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…
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…
Decentralized Learning Made Practical with Client Sampling
Martijn de Vos, Akash Dhasade, Anne-Marie Kermarrec +3
Decentralized learning (DL) leverages edge devices for collaborative model training while avoiding coordination by a central server. Due to privacy concerns, DL has become an attra…