77 citations · 179 across the 54 of their papers we have counts for
6 papers · 1 filter
Private Inference in Quantized Models
Zirui Deng, Vinayak Ramkumar, Rawad Bitar +1
A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding…
Sparsity and Privacy in Secret Sharing: A Fundamental Trade-Off
Rawad Bitar, Maximilian Egger, Antonia Wachter-Zeh +1
This work investigates the design of sparse secret sharing schemes that encode a sparse private matrix into sparse shares. This investigation is motivated by distributed computing,…
Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance
Maximilian Egger, Marvin Xhemrishi, Antonia Wachter-Zeh +1
This paper considers the problem of outsourcing the multiplication of two private and sparse matrices to untrusted workers. Secret sharing schemes can be used to tolerate straggler…
Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion
Maximilian Egger, Christoph Hofmeister, Antonia Wachter-Zeh +1
In federated learning, a federator coordinates the training of a model, e.g., a neural network, on privately owned data held by several participating clients. The gradient descent…
Fast and Straggler-Tolerant Distributed SGD with Reduced Computation Load
Maximilian Egger, Serge Kas Hanna, Rawad Bitar
In distributed machine learning, a central node outsources computationally expensive calculations to external worker nodes. The properties of optimization procedures like stochasti…
Trading Communication for Computation in Byzantine-Resilient Gradient Coding
Christoph Hofmeister, Luis Maßny, Eitan Yaakobi +1
We consider gradient coding in the presence of an adversary, controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes…