Add to ORKGWe focus on the problem of efficiently deploying a federated learning training task in a decentralized setting with multiple aggregators. To that end, we introduce a number of improvements and modifications to the recently proposed IPLS protocol. In particular, we relax its assumption for direct communication across participants, using instead indirect communication over a decentralized storage system, effectively turning it into a partially asynchronous protocol. Moreover, we secure it against malicious aggregators (that drop or alter data) by relying on homomorphic cryptographic commitments for efficient verification of aggregation. We implement the modified IPLS protocol and report on its performance and potential bottlenecks. Finally,...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge d...
The rapid development in network technology has resulted in the proliferation of Internet of Things ...
Federated learning (FL) is a promising technical support to the vision of ubiquitous artificial inte...
Federated learning enables the training of machine learning models on isolated data islands but also...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
International audienceThe development and adoption of personal data management systems (PDMS) has be...
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' lo...
Federated learning (FL) is a type of machine learning where devices locally train a model on their p...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated Learning (FL) has emerged as a powerful paradigm to train collaborative machine learning (...
Machine learning algorithms are undoubtedly one of the most popular algorithms in recent years, and ...
Federated learning has emerged as a privacy-preserving machine learning approach where multiple part...
International audienceFederated learning (FL) is a distributed machine learning (ML) technique that ...
Federated learning is a privacy-enforcing machine learning technology but suffers from limited scala...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge d...
The rapid development in network technology has resulted in the proliferation of Internet of Things ...
Federated learning (FL) is a promising technical support to the vision of ubiquitous artificial inte...
Federated learning enables the training of machine learning models on isolated data islands but also...
The advent of machine learning techniques has given rise to modern devices with built-in models for ...
International audienceThe development and adoption of personal data management systems (PDMS) has be...
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' lo...
Federated learning (FL) is a type of machine learning where devices locally train a model on their p...
Federated learning is an improved version of distributed machine learning that further offloads oper...
Federated Learning (FL) has emerged as a powerful paradigm to train collaborative machine learning (...
Machine learning algorithms are undoubtedly one of the most popular algorithms in recent years, and ...
Federated learning has emerged as a privacy-preserving machine learning approach where multiple part...
International audienceFederated learning (FL) is a distributed machine learning (ML) technique that ...
Federated learning is a privacy-enforcing machine learning technology but suffers from limited scala...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge d...
The rapid development in network technology has resulted in the proliferation of Internet of Things ...