Add to ORKGFederated learning was introduced to enable machine learning over large decentralized datasets while promising privacy by eliminating the need for data sharing. Despite this, prior work has shown that shared gradients often contain private information and attackers can gain knowledge either through malicious modification of the architecture and parameters or by using optimization to approximate user data from the shared gradients. However, prior data reconstruction attacks have been limited in setting and scale, as most works target FedSGD and limit the attack to single-client gradients. Many of these attacks fail in the more practical setting of FedAVG or if updates are aggregated together using secure aggregation. Data reconstruction beco...
AI\u27s applicability across diverse fields is hindered by data sensitivity, privacy concerns, and l...
Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volu...
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables m...
Federated learning (FL) has rapidly risen in popularity due to its promise of privacy and efficiency...
Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradi...
Recent attacks have shown that user data can be recovered from FedSGD updates, thus breaking privacy...
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model w...
Machine learning (ML) algorithms require a massive amount of data. Firms such as Google and Facebook...
Recent attacks have shown that user data can be recovered from FedSGD updates, thus breaking privacy...
Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs....
Federated learning, as a distributed learning that conducts the training on the local devices withou...
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model para...
Federated learning (FL) was originally regarded as a framework for collaborative learning among clie...
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning...
Even though recent years have seen many attacks exposing severe vulnerabilities in federated learnin...
AI\u27s applicability across diverse fields is hindered by data sensitivity, privacy concerns, and l...
Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volu...
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables m...
Federated learning (FL) has rapidly risen in popularity due to its promise of privacy and efficiency...
Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradi...
Recent attacks have shown that user data can be recovered from FedSGD updates, thus breaking privacy...
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model w...
Machine learning (ML) algorithms require a massive amount of data. Firms such as Google and Facebook...
Recent attacks have shown that user data can be recovered from FedSGD updates, thus breaking privacy...
Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs....
Federated learning, as a distributed learning that conducts the training on the local devices withou...
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model para...
Federated learning (FL) was originally regarded as a framework for collaborative learning among clie...
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning...
Even though recent years have seen many attacks exposing severe vulnerabilities in federated learnin...
AI\u27s applicability across diverse fields is hindered by data sensitivity, privacy concerns, and l...
Motivated by the ever-increasing concerns on personal data privacy and the rapidly growing data volu...
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables m...