5 citations · 9 across the 4 of their papers we have counts for
4 papers
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
Hamid Mozaffari, Sunav Choudhary, Amir Houmansadr
Federated learning (FL) is a distributed machine learning paradigm that enables training models on decentralized data. The field of FL security against poisoning attacks is plagued…
FedPerm: Private and Robust Federated Learning by Parameter Permutation
Hamid Mozaffari, Virendra J. Marathe, Dave Dice
Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting clients to collaboratively train a common machine learning model. Client data privacy is…
E2FL: Equal and Equitable Federated Learning
Hamid Mozaffari, Amir Houmansadr
Federated Learning (FL) enables data owners to train a shared global model without sharing their private data. Unfortunately, FL is susceptible to an intrinsic fairness issue: due…
FRL: Federated Rank Learning
Hamid Mozaffari, Virat Shejwalkar, Amir Houmansadr
Federated learning (FL) allows mutually untrusted clients to collaboratively train a common machine learning model without sharing their private/proprietary training data among eac…