Publications by Sayan Biswas | EPFL Graph Search

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Most differentially private algorithms assume a central model in which a reliable third party inserts noise to queries made on datasets, or a local model where the data owners directly perturb their data. However, the central model is vulnerable via a sing ...

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 of hete ...

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

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 key cha ...

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 relying on a central server. This paper introduces Zip-DL, a privacy-aware ...

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 privacy ...

Sayan Biswas

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Tight Differential Privacy Guarantees for the Shuffle Model with k-Randomized Response

Fair Decentralized Learning

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Boosting Asynchronous Decentralized Learning with Model Fragmentation

Low-Cost Privacy-Preserving Decentralized Learning

Noiseless Privacy-Preserving Decentralized Learning