15 citations · 24 across the 8 of their papers we have counts for
9 papers
Backdoor Mitigation in Decentralized LLM Fine-Tuning
Sayan Biswas, Jade Garcia Bourrée, Rachid Guerraoui +5
Decentralized large language model (LLM) fine-tuning lets organizations collaboratively train a shared LLM on data they cannot pool, without a central coordinator. In every round,…
Practical and Private Hybrid ML Inference with Fully Homomorphic Encryption
Sayan Biswas, Philippe Chartier, Akash Dhasade +7
In contemporary cloud-based services, protecting users' sensitive data and ensuring the confidentiality of the server's model are critical. Fully homomorphic encryption (FHE) enabl…
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…
Navigating the Accuracy-Size Trade-Off with Flexible Model Merging
Akash Dhasade, Divyansh Jhunjhunwala, Milos Vujasinovic +2
Model merging has emerged as an efficient method to combine multiple single-task fine-tuned models. The merged model can enjoy multi-task capabilities without expensive training. W…
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…
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…
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…
Get More for Less in Decentralized Learning Systems
Akash Dhasade, Anne-Marie Kermarrec, Rafael Pires +3
Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality.…
Decentralized Learning Made Easy with DecentralizePy
Akash Dhasade, Anne-Marie Kermarrec, Rafael Pires +2
Decentralized learning (DL) has gained prominence for its potential benefits in terms of scalability, privacy, and fault tolerance. It consists of many nodes that coordinate withou…