10 citations · 18 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
Effective LoRA Adapter Routing using Task Representations
Akash Dhasade, Anne-Marie Kermarrec, Igor Pavlovic +4
Low-rank adaptation (LoRA) enables parameter efficient specialization of large language models (LLMs) through modular adapters, resulting in rapidly growing public adapter pools sp…
Robust ML Auditing using Prior Knowledge
Jade Garcia Bourrée, Augustin Godinot, Martijn De Vos +5
Among the many technical challenges to enforcing AI regulations, one crucial yet underexplored problem is the risk of audit manipulation. This manipulation occurs when a platform d…
Accelerating MoE Model Inference with Expert Sharding
Oana Balmau, Anne-Marie Kermarrec, Rafael Pires +3
Mixture of experts (MoE) models achieve state-of-the-art results in language modeling but suffer from inefficient hardware utilization due to imbalanced token routing and communica…
Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
Akash Dhasade, Rachid Guerraoui, Anne-Marie Kermarrec +4
Large language models (LLMs) achieve remarkable performance across domains but remain prone to hallucinations and inconsistencies. Retrieval-augmented generation (RAG) mitigates th…
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…