120 citations · 1.1k across the 101 of their papers we have counts for
10 papers · 1 filter
Adaptive Compression in Federated Learning via Side Information
Berivan Isik, Francesco Pase, Deniz Gunduz +3
The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL). Among existing approaches, sta…
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
Berivan Isik, Wei-Ning Chen, Ayfer Ozgur +2
We study the mean estimation problem under communication and local differential privacy constraints. While previous work has proposed \emph{order}-optimal algorithms for the same p…
Sparse Random Networks for Communication-Efficient Federated Learning
Berivan Isik, Francesco Pase, Deniz Gunduz +2
One main challenge in federated learning is the large communication cost of exchanging weight updates from clients to the server at each round. While prior work has made great prog…
An Information-Theoretic Justification for Model Pruning
Berivan Isik, Tsachy Weissman, Albert No
We study the neural network (NN) compression problem, viewing the tension between the compression ratio and NN performance through the lens of rate-distortion theory. We choose a d…
Neural Network Compression for Noisy Storage Devices
Berivan Isik, Kristy Choi, Xin Zheng +4
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Despite the significant progress in NN mo…
Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions
Yanjun Han, Zhengyuan Zhou, Aaron Flores +2
First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms. This shift ha…