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pro vyhledávání: '"Biswas, Sayan"'
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
In the context of four-dimensional ${\cal N} = 1$ type IIB superstring compactifications, the U-dual completion of the holomorphic flux superpotential leads to four S-dual pairs of fluxes, namely $(F, H), (Q, P), (P', Q')$ and $(H', F')$. It has been
Externí odkaz:
http://arxiv.org/abs/2407.15822
Autor:
Biswas, Sayan, Dras, Mark, Faustini, Pedro, Fernandes, Natasha, McIver, Annabelle, Palamidessi, Catuscia, Sadeghi, Parastoo
Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy preservation in mind. In federated learning, it has been shown that a
Externí odkaz:
http://arxiv.org/abs/2406.13569
Decentralized learning (DL) faces increased vulnerability to privacy breaches due to sophisticated attacks on machine learning (ML) models. Secure aggregation is a computationally efficient cryptographic technique that enables multiple parties to com
Externí odkaz:
http://arxiv.org/abs/2405.07708
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:
Biswas, Sayan, Frey, Davide, Gaudel, Romaric, Kermarrec, Anne-Marie, Lerévérend, Dimitri, Pires, Rafael, Sharma, Rishi, Taïani, François
This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that exploits correlated noise to provide strong privacy protection against a local adversary while yielding efficient convergence guarantees for a low communic
Externí odkaz:
http://arxiv.org/abs/2403.11795
Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minim
Externí odkaz:
http://arxiv.org/abs/2309.00416
Autor:
Biswas, Sayan, Palamidessi, Catuscia
With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate
Externí odkaz:
http://arxiv.org/abs/2206.10525