5 citations · 6 across the 4 of their papers we have counts for
4 papers
Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning
Adda Akram Bendoukha, Heber Hwang Arcolezi, Nesrine Kaaniche +1
Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, whic…
Robust Federated Learning via Byzantine Filtering over Encrypted Updates
Adda Akram Bendoukha, Aymen Boudguiga, Nesrine Kaaniche +3
Federated Learning (FL) aims to train a collaborative model while preserving data privacy. However, the distributed nature of this approach still raises privacy and security issues…
Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness
Héber H. Arcolezi, Mina Alishahi, Adda-Akram Bendoukha +1
Machine learning (ML) algorithms are heavily based on the availability of training data, which, depending on the domain, often includes sensitive information about data providers.…
Homomorphic Sortition -- Secret Leader Election for PoS Blockchains
Luciano Freitas, Andrei Tonkikh, Adda-Akram Bendoukha +4
In a single secret leader election protocol (SSLE), one of the system participants is chosen and, unless it decides to reveal itself, no other participant can identify it. SSLE has…