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04/24/2023
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
The real-world implementation of federated learning is complex and requi...
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01/04/2023
Federated Learning for Data Streams
Federated learning (FL) is an effective solution to train machine learni...
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11/21/2022
Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization
The aim of Machine Unlearning (MU) is to provide theoretical guarantees ...
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06/21/2022
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
We propose a novel framework to study asynchronous federated learning op...
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11/17/2021
Personalized Federated Learning through Local Memorization
Federated learning allows clients to collaboratively learn statistical m...
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08/23/2021
Federated Multi-Task Learning under a Mixture of Distributions
The increasing size of data generated by smartphones and IoT devices mot...
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07/26/2021
On The Impact of Client Sampling on Federated Learning Convergence
While clients' sampling is a central operation of current state-of-the-a...
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05/12/2021
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
This work addresses the problem of optimizing communications between ser...
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10/23/2020
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Federated learning usually employs a client-server architecture where an...
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06/21/2020