62 citations · 376 across the 37 of their papers we have counts for
38 papers
A Unified Benchmark for Privacy-preserving Vector Search
Anne-Marie Kermarrec, Rafael Pires, Mathis Randl +1
Vector search powers semantic search, recommendation systems, and retrieval-augmented generation (RAG). By design, the service answering a query sees both the query embedding and,…
RIVA: Leveraging LLM Agents for Reliable Configuration Drift Detection
Sami Abuzakuk, Lucas Crijns, Anne-Marie Kermarrec +2
Infrastructure as code (IaC) tools automate cloud provisioning but verifying that deployed systems remain consistent with the IaC specifications remains challenging. Such configura…
Catapults to the Rescue: Accelerating Vector Search by Exploiting Query Locality
Sami Abuzakuk, Anne-Marie Kermarrec, Rafael Pires +2
Graph-based indexing is the dominant approach for approximate nearest neighbor search in vector databases, offering high recall with low latency across billions of vectors. However…
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…
Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster
Marie Reinbigler, Rishi Sharma, Rafael Pires +3
Analyzing gigapixel images is recognized as computationally demanding. In this paper, we introduce PyramidAI, a technique for analyzing gigapixel images with reduced computational…
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…
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…
Leveraging Approximate Caching for Faster Retrieval-Augmented Generation
Shai Bergman, Anne-Marie Kermarrec, Diana Petrescu +4
Retrieval-augmented generation (RAG) improves the reliability of large language model (LLM) answers by integrating external knowledge. However, RAG increases the end-to-end inferen…
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…
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…
Boosting Asynchronous Decentralized Learning with Model Fragmentation
Sayan Biswas, Anne-Marie Kermarrec, Alexis Marouani +3
Decentralized learning (DL) is an emerging technique that allows nodes on the web to collaboratively train machine learning models without sharing raw data. Dealing with stragglers…
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…
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
Akash Dhasade, Anne-Marie Kermarrec, Tuan-Anh Nguyen +2
Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual…
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…
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…
Low-Cost Privacy-Preserving Decentralized Learning
Sayan Biswas, Davide Frey, Romaric Gaudel +5
Decentralized learning (DL) is an emerging paradigm of collaborative machine learning that enables nodes in a network to train models collectively without sharing their raw data or…
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
Martijn de Vos, Sadegh Farhadkhani, Rachid Guerraoui +3
We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence…
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…
TEE-based decentralized recommender systems: The raw data sharing redemption
Akash Dhasade, Nevena Dresevic, Anne-Marie Kermarrec +1
Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between us…
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
Mohamed Yassine Boukhari, Akash Dhasade, Anne-Marie Kermarrec +3
Federated learning (FL) enables a set of client devices to collaboratively train a model without sharing raw data. This process, though, operates under the constrained computation…
NVCache: A Plug-and-Play NVMM-based I/O Booster for Legacy Systems
Rémi Dulong, Rafael Pires, Andreia Correia +4
This paper introduces NVCache, an approach that uses a non-volatile main memory (NVMM) as a write cache to improve the write performance of legacy applications. We compare NVCache…