Add to ORKGFederated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy. Yet, existing FL solutions usually rely on a centralized aggregator for model weight aggregation, while assuming clients are honest. Even if data privacy can still be preserved, the problem of single-point failure and data poisoning attack from malicious clients remains unresolved. To tackle this challenge, we propose to use distributed ledger technology (DLT) to achieve FLock, a secure and reliable decentralized Federated Learning system built on blockchain. To guarantee model quality, we design a novel peer-to-peer (P2P) review and reward/slash mechanism to detect and deter ...
Federated Learning (FL), which allows multiple participants to co-train machine Learning models with...
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' lo...
Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. How...
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the curr...
The growth of information technology has resulted in a massive escalation of data and the demand for...
The rapid expansion of data worldwide invites the need for more distributed solutions in order to ap...
peer reviewedFederated machine learning (FL) allows to collectively train models on sensitive data a...
International audienceFederated learning (FL) is a distributed machine learning (ML) technique that ...
Federated learning (FL) is a promising framework for distributed machine learning that trains models...
Federated learning (FL) is a promising framework for distributed machine learning that trains models...
The advent of federated learning has facilitated large-scale data exchange amongst machine learning ...
Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which malic...
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model w...
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning...
Federated learning enables multiple users to collaboratively train a global model using the users’ p...
Federated Learning (FL), which allows multiple participants to co-train machine Learning models with...
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' lo...
Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. How...
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the curr...
The growth of information technology has resulted in a massive escalation of data and the demand for...
The rapid expansion of data worldwide invites the need for more distributed solutions in order to ap...
peer reviewedFederated machine learning (FL) allows to collectively train models on sensitive data a...
International audienceFederated learning (FL) is a distributed machine learning (ML) technique that ...
Federated learning (FL) is a promising framework for distributed machine learning that trains models...
Federated learning (FL) is a promising framework for distributed machine learning that trains models...
The advent of federated learning has facilitated large-scale data exchange amongst machine learning ...
Due to its distributed nature, federated learning is vulnerable to poisoning attacks, in which malic...
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model w...
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning...
Federated learning enables multiple users to collaboratively train a global model using the users’ p...
Federated Learning (FL), which allows multiple participants to co-train machine Learning models with...
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' lo...
Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. How...