{"total":66,"rows":[{"id":"2501.06322","title":"Multi-Agent Collaboration Mechanisms: A Survey of LLMs","created":"2025-01-10","cited_by_count":431,"area":"agents"},{"id":"2306.08153","title":"(Amplified) Banded Matrix Factorization: A unified approach to private training","created":"2023-06-13","cited_by_count":49,"area":null},{"id":"2310.09266","title":"User Inference Attacks on Large Language Models","created":"2023-10-13","cited_by_count":36,"area":null},{"id":"2509.17488","title":"Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents","created":"2025-09-22","cited_by_count":36,"area":"agents"},{"id":"2403.00871","title":"Teach LLMs to Phish: Stealing Private Information from Language Models","created":"2024-03-01","cited_by_count":32,"area":null},{"id":"2401.04472","title":"A Survey on Efficient Federated Learning Methods for Foundation Model Training","created":"2024-01-09","cited_by_count":29,"area":null},{"id":"2406.02958","title":"PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs","created":"2024-06-05","cited_by_count":29,"area":null},{"id":"2407.07737","title":"Fine-Tuning Large Language Models with User-Level Differential Privacy","created":"2024-07-10","cited_by_count":29,"area":null},{"id":"2308.12247","title":"How to Protect Copyright Data in Optimization of Large Language Models?","created":"2023-08-23","cited_by_count":26,"area":null},{"id":"2311.17431","title":"Grounding Foundation Models through Federated Transfer Learning: A General Framework","created":"2023-11-29","cited_by_count":24,"area":null},{"id":"2310.06771","title":"Correlated Noise Provably Beats Independent Noise for Differentially Private Learning","created":"2023-10-10","cited_by_count":23,"area":null},{"id":"2311.12275","title":"Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis","created":"2023-11-21","cited_by_count":20,"area":null},{"id":"2403.00932","title":"Differentially Private Knowledge Distillation via Synthetic Text Generation","created":"2024-03-01","cited_by_count":18,"area":"distill"},{"id":"2404.04360","title":"Prompt Public Large Language Models to Synthesize Data for Private On-device Applications","created":"2024-04-05","cited_by_count":17,"area":null},{"id":"2404.16706","title":"Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy","created":"2024-04-25","cited_by_count":17,"area":null},{"id":"2406.03777","title":"Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices","created":"2024-06-06","cited_by_count":17,"area":null},{"id":"2503.12347","title":"Synthesizing Privacy-Preserving Text Data via Finetuning without Finetuning Billion-Scale LLMs","created":"2025-03-16","cited_by_count":16,"area":null},{"id":"2410.08892","title":"Federated Learning in Practice: Reflections and Projections","created":"2024-10-11","cited_by_count":15,"area":null},{"id":"2308.06239","title":"Private Distribution Learning with Public Data: The View from Sample Compression","created":"2023-08-11","cited_by_count":14,"area":null},{"id":"2406.12844","title":"Synergizing Foundation Models and Federated Learning: A Survey","created":"2024-06-18","cited_by_count":14,"area":null},{"id":"2504.16438","title":"POPri: Private Federated Learning using Preference-Optimized Synthetic Data","created":"2025-04-23","cited_by_count":14,"area":"alignment"},{"id":"2405.03636","title":"The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape","created":"2024-05-06","cited_by_count":13,"area":null},{"id":"2310.12462","title":"Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights","created":"2023-10-19","cited_by_count":12,"area":null},{"id":"2402.13531","title":"Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation","created":"2024-02-21","cited_by_count":12,"area":null},{"id":"2306.03235","title":"Information Flow Control in Machine Learning through Modular Model Architecture","created":"2023-06-05","cited_by_count":11,"area":null},{"id":"2310.16538","title":"FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning","created":"2023-10-25","cited_by_count":10,"area":null},{"id":"2405.06368","title":"DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation","created":"2024-05-10","cited_by_count":9,"area":null},{"id":"2408.08868","title":"A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs","created":"2024-08-16","cited_by_count":9,"area":null},{"id":"2404.06430","title":"pfl-research: simulation framework for accelerating research in Private Federated Learning","created":"2024-04-09","cited_by_count":8,"area":null},{"id":"2407.14367","title":"Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations","created":"2024-07-19","cited_by_count":8,"area":null},{"id":"2409.09558","title":"A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem","created":"2024-09-14","cited_by_count":8,"area":null},{"id":"2308.03573","title":"When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection","created":"2023-08-07","cited_by_count":7,"area":null},{"id":"2409.17754","title":"Byzantine-Robust Aggregation for Securing Decentralized Federated Learning","created":"2024-09-26","cited_by_count":7,"area":null},{"id":"2502.05087","title":"Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2307.15017","title":"Samplable Anonymous Aggregation for Private Federated Data Analysis","created":"2023-07-27","cited_by_count":6,"area":null},{"id":"2311.13624","title":"A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformer","created":"2023-11-22","cited_by_count":6,"area":null},{"id":"2412.19916","title":"On the Convergence of DP-SGD with Adaptive Clipping","created":"2024-12-27","cited_by_count":6,"area":null},{"id":"2307.10999","title":"Private Federated Learning with Autotuned Compression","created":"2023-07-20","cited_by_count":5,"area":null},{"id":"2404.10764","title":"Confidential Federated Computations","created":"2024-04-16","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":"2402.09247","title":"Momentum Approximation in Asynchronous Private Federated Learning","created":"2024-02-14","cited_by_count":3,"area":null},{"id":"2412.14396","title":"Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis","created":"2024-12-18","cited_by_count":3,"area":null},{"id":"2503.12314","title":"Empirical Privacy Variance","created":"2025-03-16","cited_by_count":3,"area":null},{"id":"2510.18232","title":"ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control","created":"2025-10-21","cited_by_count":3,"area":null},{"id":"2403.08100","title":"Efficient Language Model Architectures for Differentially Private Federated Learning","created":"2024-03-12","cited_by_count":2,"area":null},{"id":"2505.18488","title":"Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications","created":"2025-05-24","cited_by_count":2,"area":null},{"id":"2506.13974","title":"Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability","created":"2025-06-16","cited_by_count":2,"area":null},{"id":"2605.09794","title":"LLM Agents Enable User-Governed Personalization Beyond Platform Boundaries","created":"2026-05-10","cited_by_count":2,"area":"agents"},{"id":"2310.16678","title":"Robust and Actively Secure Serverless Collaborative Learning","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2312.08298","title":"Venn: Resource Management for Collaborative Learning Jobs","created":"2023-12-13","cited_by_count":1,"area":null},{"id":"2407.14710","title":"Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence","created":"2024-07-20","cited_by_count":1,"area":null},{"id":"2410.01209","title":"Debiasing Federated Learning with Correlated Client Participation","created":"2024-10-02","cited_by_count":1,"area":null},{"id":"2502.05765","title":"Privacy-Preserving Dataset Combination","created":"2025-02-09","cited_by_count":1,"area":null},{"id":"2504.09850","title":"Accelerating Differentially Private Federated Learning via Adaptive Extrapolation","created":"2025-04-14","cited_by_count":1,"area":null},{"id":"2504.18916","title":"UnifyFL: Enabling Decentralized Cross-Silo Federated Learning","created":"2025-04-26","cited_by_count":1,"area":null},{"id":"2509.08709","title":"Securing Private Federated Learning in a Malicious Setting: A Scalable TEE-Based Approach with Client Auditing","created":"2025-09-10","cited_by_count":1,"area":null},{"id":"2510.07304","title":"Cocoon: A System Architecture for Differentially Private Training with Correlated Noises","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2503.16233","title":"Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2509.16180","title":"Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2511.21876","title":"Differential privacy from axioms","created":"2025-11-26","cited_by_count":0,"area":null},{"id":"2601.09460","title":"SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacy","created":"2026-01-14","cited_by_count":0,"area":null},{"id":"2603.19258","title":"MAPLE: Metadata Augmented Private Language Evolution","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2609.00492","title":"The Privacy-Hallucination Tradeoff in Differentially Private Language Models","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.03800","title":"Govern the Model, Not Only the Data: Storage, Circulation, and Learning in Creative AI","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2610.00170","title":"On-Device Commercial Intent Retrieval Under Size, Latency, and Privacy Constraints: A 3 MiB Retrieval System with Typed Egress Boundaries","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2610.17120","title":"Can R\\'enyi Accountants Beat Lossless Accountants for DP-SGD?","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}