Ahmad Rammal — accepted AI-conference papers

2025

Correlated Quantization for Faster Nonconvex Distributed Optimization

Andrei Panferov, Yury Demidovich, Ahmad Rammal, Peter Richtárik

UAI 2025

Quantization [Alistarh et al., 2017] is an important (stochastic) compression technique that reduces the volume of transmitted bits during each communication round in distributed model training. Suresh et al. [2022] introduce correlated quantizers and show their advantages over independent counterpa

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2024

Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates

Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtarik

AISTATS 2024poster

Byzantine robustness is an essential feature of algorithms for certain distributed optimization problems, typically encountered in collaborative/federated learning. These problems are usually huge-scale, implying that communication compression is also imperative for their resolution. These factors h…

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