{"total":66,"rows":[{"id":"2107.06917","title":"A Field Guide to Federated Optimization","created":"2021-07-14","cited_by_count":315,"area":null},{"id":"2111.12444","title":"Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications","created":"2021-11-24","cited_by_count":97,"area":null},{"id":"2108.00951","title":"Rethinking gradient sparsification as total error minimization","created":"2021-08-02","cited_by_count":41,"area":null},{"id":"2203.03761","title":"The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning","created":"2022-03-07","cited_by_count":39,"area":null},{"id":"2205.02466","title":"Optimal Algorithms for Mean Estimation under Local Differential Privacy","created":"2022-05-05","cited_by_count":36,"area":null},{"id":"2110.12805","title":"Algorithms for the Communication of Samples","created":"2021-10-25","cited_by_count":34,"area":null},{"id":"2206.09888","title":"SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression","created":"2022-06-20","cited_by_count":34,"area":null},{"id":"2209.00230","title":"Trading Off Privacy, Utility and Efficiency in Federated Learning","created":"2022-09-01","cited_by_count":33,"area":null},{"id":"2102.12099","title":"Lossless Compression of Efficient Private Local Randomizers","created":"2021-02-24","cited_by_count":27,"area":null},{"id":"2310.11028","title":"Matrix Compression via Randomized Low Rank and Low Precision Factorization","created":"2023-10-17","cited_by_count":25,"area":null},{"id":"2111.00092","title":"Optimal Compression of Locally Differentially Private Mechanisms","created":"2021-10-29","cited_by_count":22,"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":"2203.08134","title":"Privacy-Aware Compression for Federated Data Analysis","created":"2022-03-15","cited_by_count":17,"area":null},{"id":"2211.03757","title":"Discrete Distribution Estimation under User-level Local Differential Privacy","created":"2022-11-07","cited_by_count":17,"area":null},{"id":"2203.04925","title":"Correlated quantization for distributed mean estimation and optimization","created":"2022-03-09","cited_by_count":14,"area":null},{"id":"2306.04444","title":"Fast Optimal Locally Private Mean Estimation via Random Projections","created":"2023-06-07","cited_by_count":14,"area":null},{"id":"2111.05454","title":"DP-REC: Private & Communication-Efficient Federated Learning","created":"2021-11-09","cited_by_count":10,"area":null},{"id":"2110.08477","title":"FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation","created":"2021-10-16","cited_by_count":9,"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":"2104.01808","title":"Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming","created":"2021-04-05","cited_by_count":8,"area":null},{"id":"2210.13386","title":"Contraction of Locally Differentially Private Mechanisms","created":"2022-10-24","cited_by_count":8,"area":null},{"id":"2307.15835","title":"Mean Estimation with User-level Privacy under Data Heterogeneity","created":"2023-07-28","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":"2110.07751","title":"Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation","created":"2021-10-14","cited_by_count":7,"area":null},{"id":"2205.13341","title":"QUIC-FL: Quick Unbiased Compression for Federated Learning","created":"2022-05-26","cited_by_count":7,"area":null},{"id":"2204.03919","title":"Network Shuffling: Privacy Amplification via Random Walks","created":"2022-04-08","cited_by_count":6,"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":"2510.25657","title":"Subgraph Federated Learning via Spectral Methods","created":"2025-10-29","cited_by_count":6,"area":null},{"id":"2103.07578","title":"Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget","created":"2021-03-13","cited_by_count":5,"area":null},{"id":"2307.10999","title":"Private Federated Learning with Autotuned Compression","created":"2023-07-20","cited_by_count":5,"area":null},{"id":"2403.17868","title":"An invitation to the sample complexity of quantum hypothesis testing","created":"2024-03-26","cited_by_count":5,"area":null},{"id":"2404.10201","title":"Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages","created":"2024-04-16","cited_by_count":5,"area":null},{"id":"2106.08597","title":"Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints","created":"2021-06-16","cited_by_count":4,"area":null},{"id":"2304.04641","title":"Probably Approximately Correct Federated Learning","created":"2023-04-10","cited_by_count":4,"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":"2407.03289","title":"Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation","created":"2024-07-03","cited_by_count":4,"area":null},{"id":"2504.17421","title":"Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks","created":"2025-04-24","cited_by_count":4,"area":null},{"id":"2109.05222","title":"Fundamental limits of over-the-air optimization: Are analog schemes optimal?","created":"2021-09-11","cited_by_count":3,"area":null},{"id":"2111.08277","title":"Wyner-Ziv Gradient Compression for Federated Learning","created":"2021-11-16","cited_by_count":3,"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":"2307.11947","title":"Collaboratively Learning Linear Models with Structured Missing Data","created":"2023-07-22","cited_by_count":3,"area":null},{"id":"2310.18868","title":"Correlation Aware Sparsified Mean Estimation Using Random Projection","created":"2023-10-29","cited_by_count":3,"area":null},{"id":"2312.05645","title":"Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity","created":"2023-12-09","cited_by_count":3,"area":null},{"id":"2402.19163","title":"Decoupled Subgraph Federated Learning","created":"2024-02-29","cited_by_count":3,"area":null},{"id":"2405.17079","title":"Learning with User-Level Local Differential Privacy","created":"2024-05-27","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":"2302.11152","title":"Multi-Message Shuffled Privacy in Federated Learning","created":"2023-02-22","cited_by_count":2,"area":null},{"id":"2402.10816","title":"TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data","created":"2024-02-16","cited_by_count":2,"area":null},{"id":"2407.11274","title":"Private Estimation when Data and Privacy Demands are Correlated","created":"2024-07-15","cited_by_count":2,"area":null},{"id":"2202.11277","title":"Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms","created":"2022-02-23","cited_by_count":1,"area":null},{"id":"2206.00707","title":"Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints","created":"2022-06-01","cited_by_count":1,"area":null},{"id":"2303.04744","title":"Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction","created":"2023-02-05","cited_by_count":1,"area":null},{"id":"2306.09547","title":"Training generative models from privatized data","created":"2023-06-15","cited_by_count":1,"area":null},{"id":"2307.07941","title":"Optimal Compression of Unit Norm Vectors in the High Distortion Regime","created":"2023-07-16","cited_by_count":1,"area":null},{"id":"2312.14712","title":"Balancing Privacy, Robustness, and Efficiency in Machine Learning","created":"2023-12-22","cited_by_count":1,"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":"2501.03222","title":"Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization","created":"2025-01-06","cited_by_count":1,"area":null},{"id":"2503.11897","title":"PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors","created":"2025-03-14","cited_by_count":1,"area":null},{"id":"2206.10565","title":"sqSGD: Locally Private and Communication Efficient Federated Learning","created":"2022-06-21","cited_by_count":0,"area":null},{"id":"2207.09916","title":"The Poisson binomial mechanism for secure and private federated learning","created":"2022-07-09","cited_by_count":0,"area":null},{"id":"2506.03746","title":"Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2507.00920","title":"Privacy-Preserving Quantized Federated Learning with Diverse Precision","created":"2025-07-01","cited_by_count":0,"area":null},{"id":"2604.28032","title":"Shuffling-Aware Optimization for Private Vector Mean Estimation","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2607.27815","title":"Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.05127","title":"SSTQ:Privacy-Preserving Vector Quantization via Subsampled Stochastic TurboQuant","created":"2026-08-05","cited_by_count":0,"area":null}],"next_cursor":null}