Optimal Energy Allocation for Delay-Constrained Traffic over Fading Multiple Access Channels. - Antonious M. Girgis; Amr El-Keyi; Mohammed Nafie (GLOBECOM, 2016)

Proactive Location-Based Scheduling of Delay-Constrained Traffic over Fading Channels. - Antonious M. Girgis; Amr El-Keyi; Mohammed Nafie; Ramy H. Gohary (VTC Fall, 2016)

Decentralized coded caching in wireless networks: Trade-off between storage and latency. - Antonious M. Girgis; Özgür Erçetin; Mohammed Nafie; Tamer A. ElBatt (ISIT, 2017)

Degrees of Freedom of Interference Networks with Transmitter-Side Caches. - Antonious M. Girgis; Özgür Erçetin; Mohammed Nafie; Tamer A. ElBatt (CoRR, 2017)

A Converse Bound for Cache-Aided Interference Networks. - Antonious M. Girgis; Özgür Erçetin; Mohammed Nafie; Tamer A. ElBatt (ACSSC, 2018)

Coded Caching and Spatial Multiplexing Gains in MIMO Interference Networks. - Antonious M. Girgis; Özgür Erçetin; Mohammed Nafie; Tamer A. ElBatt (WCNC, 2019)

Fundamental Limits of Memory-Latency Tradeoff in Fog Radio Access Networks Under Arbitrary Demands. - Antonious M. Girgis; Özgür Erçetin; Mohammed Nafie; Tamer A. ElBatt (IEEE Trans. Wirel. Commun., 2019)

Hiding Identities: Estimation Under Local Differential Privacy. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ISIT, 2020)

Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Peter Kairouz; Ananda Theertha Suresh (CoRR, 2020)

Successive Refinement of Privacy. - Antonious M. Girgis; Deepesh Data; Kamalika Chaudhuri; Christina Fragouli; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2020)

Shuffled Model of Differential Privacy in Federated Learning. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Peter Kairouz; Ananda Theertha Suresh (AISTATS, 2021)

On the Rényi Differential Privacy of the Shuffle Model. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Ananda Theertha Suresh; Peter Kairouz (CCS, 2021)

Differentially Private Federated Learning with Shuffling and Client Self-Sampling. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ISIT, 2021)

Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (NeurIPS, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 2021)

Shuffled Model of Federated Learning: Privacy, Accuracy and Communication Trade-Offs. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi; Peter Kairouz; Ananda Theertha Suresh (IEEE J. Sel. Areas Inf. Theory, 2021)

Distributed User-Level Private Mean Estimation. - Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ISIT, 2022)

A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy. - Kaan Ozkara; Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (CoRR, 2022)

A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. - Kaan Ozkara; Antonious M. Girgis; Deepesh Data; Suhas N. Diggavi (ICLR, 2023)

Communication-Efficient and Private Distributed Learning - Antonious M. Girgis (2023)

Fundamental Limits of Cache-Aided MIMO Wireless Networks. - Antonious M. Girgis; Özgür Erçetin; Mohammed Nafie; Tamer A. ElBatt (IEEE Trans. Inf. Theory, 2023)

Learning from straggler clients in federated learning. - Andrew Hard; Antonious M. Girgis; Ehsan Amid; Sean Augenstein; Lara McConnaughey; Rajiv Mathews; Rohan Anil (CoRR, 2024)

Multi-Message Shuffled Privacy in Federated Learning. - Antonious M. Girgis; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2024)

Differentially Private Stochastic Linear Bandits: (Almost) for Free. - Osama A. Hanna; Antonious M. Girgis; Christina Fragouli; Suhas N. Diggavi (IEEE J. Sel. Areas Inf. Theory, 2024)