48 citations · 50 across the 8 of their papers we have counts for
19 papers
Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes
Emre Ozfatura, Yulin Shao, Amin Ghazanfari +3
Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are ga…
Semi-Decentralized Federated Learning with Collaborative Relaying
Michal Yemini, Rajarshi Saha, Emre Ozfatura +2
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS). At every com…
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…
Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning
Baturalp Buyukates, Emre Ozfatura, Sennur Ulukus +1
Distributed implementations are crucial in speeding up large scale machine learning applications. Distributed gradient descent (GD) is widely employed to parallelize the learning t…
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…
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…