Zobrazeno 1 - 10
of 45
pro vyhledávání: '"de Vos, Martijn"'
Autor:
Biswas, Sayan, Kermarrec, Anne-Marie, Marouani, Alexis, Pires, Rafael, Sharma, Rishi, De Vos, Martijn
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, i.e., nodes with slower compute or communication than others, is a k
Externí odkaz:
http://arxiv.org/abs/2410.12918
Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level o
Externí odkaz:
http://arxiv.org/abs/2410.02541
Autor:
Guerraoui, Rachid, Kermarrec, Anne-Marie, Kucherenko, Anastasiia, Pinot, Rafael, de Vos, Martijn
The ability of a peer-to-peer (P2P) system to effectively host decentralized applications often relies on the availability of a peer-sampling service, which provides each participant with a random sample of other peers. Despite the practical effectiv
Externí odkaz:
http://arxiv.org/abs/2408.03829
Autor:
Dhasade, Akash, Dini, Paolo, Guerra, Elia, Kermarrec, Anne-Marie, Miozzo, Marco, Pires, Rafael, Sharma, Rishi, de Vos, Martijn
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 iterative rounds of DL, models are trained locally, shared with neighbo
Externí odkaz:
http://arxiv.org/abs/2407.01283
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 model updates by nodes also diminishes. In this study, we increase th
Externí odkaz:
http://arxiv.org/abs/2405.15644
Autor:
Biswas, Sayan, Even, Mathieu, Kermarrec, Anne-Marie, Massoulie, Laurent, Pires, Rafael, Sharma, Rishi, de Vos, Martijn
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 to infer training data. Conventional defenses such as differential pr
Externí odkaz:
http://arxiv.org/abs/2404.09536
Autor:
de Vos, Martijn, Dhasade, Akash, Bourrée, Jade Garcia, Kermarrec, Anne-Marie, Merrer, Erwan Le, Rottembourg, Benoit, Tredan, Gilles
Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strat
Externí odkaz:
http://arxiv.org/abs/2402.08522
Autor:
de Vos, Martijn, Farhadkhani, Sadegh, Guerraoui, Rachid, Kermarrec, Anne-Marie, Pires, Rafael, Sharma, Rishi
We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node
Externí odkaz:
http://arxiv.org/abs/2310.01972
Autor:
de Vos, Martijn, Dhasade, Akash, Kermarrec, Anne-Marie, Lavoie, Erick, Pouwelse, Johan, Sharma, Rishi
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 attractive alternative to centralized learning schemes since training data
Externí odkaz:
http://arxiv.org/abs/2302.13837
Catalyzed by the popularity of blockchain technology, there has recently been a renewed interest in the design, implementation and evaluation of decentralized systems. Most of these systems are intended to be deployed at scale and in heterogeneous en
Externí odkaz:
http://arxiv.org/abs/2301.04508