{"total":22,"rows":[{"id":"2305.19185","title":"Compression with Bayesian Implicit Neural Representations","created":"2023-05-30","cited_by_count":25,"area":null},{"id":"2304.01541","title":"Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation","created":"2023-04-04","cited_by_count":22,"area":null},{"id":"2310.20682","title":"Compression with Exact Error Distribution for Federated Learning","created":"2023-10-31","cited_by_count":11,"area":null},{"id":"2203.00194","title":"Private Frequency Estimation via Projective Geometry","created":"2022-03-01","cited_by_count":9,"area":null},{"id":"2306.04924","title":"Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation","created":"2023-06-08","cited_by_count":9,"area":null},{"id":"2309.07809","title":"Communication Efficient Private Federated Learning Using Dithering","created":"2023-09-14","cited_by_count":9,"area":null},{"id":"2210.13386","title":"Contraction of Locally Differentially Private Mechanisms","created":"2022-10-24","cited_by_count":8,"area":null},{"id":"2405.20782","title":"Universal Exact Compression of Differentially Private Mechanisms","created":"2024-05-28","cited_by_count":8,"area":null},{"id":"2211.03942","title":"Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design","created":"2022-11-08","cited_by_count":6,"area":null},{"id":"2306.12625","title":"Adaptive Compression in Federated Learning via Side Information","created":"2023-06-22","cited_by_count":6,"area":null},{"id":"2405.02341","title":"Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy","created":"2024-05-02","cited_by_count":4,"area":null},{"id":"2405.04363","title":"Some Notes on the Sample Complexity of Approximate Channel Simulation","created":"2024-05-07","cited_by_count":4,"area":null},{"id":"2407.03289","title":"Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation","created":"2024-07-03","cited_by_count":4,"area":null},{"id":"2302.09624","title":"Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy","created":"2023-02-19","cited_by_count":3,"area":null},{"id":"2411.08791","title":"Locally Private Sampling with Public Data","created":"2024-11-13","cited_by_count":3,"area":null},{"id":"2607.03392","title":"Scalable Differentially Private Data Compression via Diffusion and Stochastic Codes","created":"2026-07-03","cited_by_count":3,"area":null},{"id":"2501.12046","title":"Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning","created":"2025-01-21","cited_by_count":2,"area":null},{"id":"2406.18145","title":"Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling","created":"2024-06-26","cited_by_count":1,"area":null},{"id":"2601.10701","title":"Communication-Efficient and Privacy-Adaptable Mechanism -- a Federated Learning Scheme with Convergence Analysis","created":"2026-01-15","cited_by_count":1,"area":null},{"id":"2603.10750","title":"Deep Randomized Distributed Function Computation (DeepRDFC): Neural Distributed Channel Simulation","created":"2026-03-11","cited_by_count":1,"area":null},{"id":"2604.20245","title":"Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2609.12067","title":"Scalable Discrete-to-Continuous Channel Simulation for Compression and Privacy","created":"2026-09-10","cited_by_count":0,"area":null}],"next_cursor":null}