Mini-batch gradient descent: Faster convergence under data sparsity. - Sarit Khirirat; Hamid Reza Feyzmahdavian; Mikael Johansson 001 (CDC, 2017)

Gradient compression for communication-limited convex optimization. - Sarit Khirirat; Mikael Johansson 001; Dan Alistarh (CDC, 2018)

The Convergence of Sparsified Gradient Methods. - Dan Alistarh; Torsten Hoefler; Mikael Johansson 001; Nikola Konstantinov; Sarit Khirirat; Cédric Renggli (NeurIPS, 2018)

Convergence Bounds for Compressed Gradient Methods with Memory Based Error Compensation. - Sarit Khirirat; Sindri Magnússon; Mikael Johansson 001 (ICASSP, 2019)

A Flexible Framework for Communication-Efficient Machine Learning. - Sarit Khirirat; Sindri Magnússon; Arda Aytekin; Mikael Johansson 001 (AAAI, 2021)

Improved Step-Size Schedules for Noisy Gradient Methods. - Sarit Khirirat; Xiaoyu Wang 008; Sindri Magnússon; Mikael Johansson 001 (ICASSP, 2021)

Compressed Gradient Methods With Hessian-Aided Error Compensation. - Sarit Khirirat; Sindri Magnússon; Mikael Johansson 001 (IEEE Trans. Signal Process., 2021)

Zeroth-Order Randomized Subspace Newton Methods. - Erik Berglund 004; Sarit Khirirat; Xiaoyu Wang 008 (ICASSP, 2022)

Eco-Fedsplit: Federated Learning with Error-Compensated Compression. - Sarit Khirirat; Sindri Magnússon; Mikael Johansson 001 (ICASSP, 2022)

First-Order Algorithms for Communication Efficient Distributed Learning. - Sarit Khirirat (2022)

Improving Performance of Private Federated Models in Medical Image Analysis. - Xiangjian Hou; Sarit Khirirat; Mohammad Yaqub; Samuel Horváth (CoRR, 2023)

Clip21: Error Feedback for Gradient Clipping. - Sarit Khirirat; Eduard Gorbunov; Samuel Horváth; Rustem Islamov; Fakhri Karray; Peter Richtárik (CoRR, 2023)

Improved Step-Size Schedules for Proximal Noisy Gradient Methods. - Sarit Khirirat; Xiaoyu Wang 008; Sindri Magnússon; Mikael Johansson 001 (IEEE Trans. Signal Process., 2023)

Compressed Federated Reinforcement Learning with a Generative Model. - Ali Beikmohammadi; Sarit Khirirat; Sindri Magnússon (ECML/PKDD (4), 2024)

Distributed Momentum Methods Under Biased Gradient Estimations. - Ali Beikmohammadi; Sarit Khirirat; Sindri Magnússon (CoRR, 2024)

Error Feedback under (L0,L1)-Smoothness: Normalization and Momentum. - Sarit Khirirat; Abdurakhmon Sadiev; Artem Riabinin; Eduard Gorbunov; Peter Richtárik (CoRR, 2024)

Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis. - Ali Beikmohammadi; Sarit Khirirat; Peter Richtárik; Sindri Magnússon (ECML/PKDD (6), 2025)

Communication-Adaptive-Gradient Sparsification for Federated Learning With Error Compensation. - Shubham Vaishnav; Sarit Khirirat; Sindri Magnússon (IEEE Internet Things J., 2025)

Smoothed Normalization for Efficient Distributed Private Optimization. - Egor Shulgin; Sarit Khirirat; Peter Richtárik (CoRR, 2025)

On the Convergence of Federated Learning Algorithms Without Data Similarity. - Ali Beikmohammadi; Sarit Khirirat; Sindri Magnússon (IEEE Trans. Big Data, 2025)

Parallel Momentum Methods Under Biased Gradient Estimations. - Ali Beikmohammadi; Sarit Khirirat; Sindri Magnússon (IEEE Trans. Control. Netw. Syst., 2025)