17 citations · 18 across the 7 of their papers we have counts for
6 papers · 1 filter
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning
Emre Ozfatura, Kerem Ozfatura, Baturalp Buyukates +3
In federated learning (FL), profiling and verifying each client is inherently difficult, which introduces a significant security vulnerability: malicious clients, commonly referred…
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
Kerem Ozfatura, Emre Ozfatura, Alptekin Kupcu +1
The increasing popularity of the federated learning (FL) framework due to its success in a wide range of collaborative learning tasks also induces certain security concerns. Among…
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks
Emre Ozfatura, Muhammad Zaid Hameed, Kerem Ozfatura +1
A common observation regarding adversarial attacks is that they mostly give rise to false activation at the penultimate layer to fool the classifier. Assuming that these activation…
Time-Correlated Sparsification for Communication-Efficient Federated Learning
Emre Ozfatura, Kerem Ozfatura, Deniz Gunduz
Federated learning (FL) enables multiple clients to collaboratively train a shared model without disclosing their local datasets. This is achieved by exchanging local model updates…
FedADC: Accelerated Federated Learning with Drift Control
Kerem Ozfatura, Emre Ozfatura, Deniz Gunduz
Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradi…
Distributed Sparse SGD with Majority Voting
Kerem Ozfatura, Emre Ozfatura, Deniz Gunduz
Distributed learning, particularly variants of distributed stochastic gradient descent (DSGD), are widely employed to speed up training by leveraging computational resources of sev…