{"list":"other","total":51,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"id":"1705.10467","title":"Federated Multi-Task Learning","created":"2017-05-30","cited_by_count":672},{"id":"2012.04221","title":"Ditto: Fair and Robust Federated Learning Through Personalization","created":"2020-12-08","cited_by_count":672},{"id":"1702.07476","title":"Renyi Differential Privacy","created":"2017-02-24","cited_by_count":650},{"id":"2006.08848","title":"Personalized Federated Learning with Moreau Envelopes","created":"2020-06-16","cited_by_count":618},{"id":"2006.04088","title":"An Efficient Framework for Clustered Federated Learning","created":"2020-06-07","cited_by_count":511},{"id":"1910.01991","title":"Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints","created":"2019-10-04","cited_by_count":503},{"id":"1806.01246","title":"ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models","created":"2018-06-04","cited_by_count":498},{"id":"1712.07557","title":"Differentially Private Federated Learning: A Client Level Perspective","created":"2017-12-20","cited_by_count":459},{"id":"1910.03581","title":"FedMD: Heterogenous Federated Learning via Model Distillation","created":"2019-10-08","cited_by_count":459},{"id":"1902.00146","title":"Agnostic Federated Learning","created":"2019-02-01","cited_by_count":456},{"id":"1905.10497","title":"Fair Resource Allocation in Federated Learning","created":"2019-05-25","cited_by_count":394},{"id":"1605.02065","title":"Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds","created":"2016-05-06","cited_by_count":346},{"id":"1702.07464","title":"Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning","created":"2017-02-24","cited_by_count":334},{"id":"2002.10619","title":"Three Approaches for Personalization with Applications to Federated Learning","created":"2020-02-25","cited_by_count":325},{"id":"1905.12101","title":"Differential Privacy Has Disparate Impact on Model Accuracy","created":"2019-05-28","cited_by_count":316},{"id":"1908.11229","title":"White-box vs Black-box: Bayes Optimal Strategies for Membership Inference","created":"2019-08-29","cited_by_count":271},{"id":"1812.00535","title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning","created":"2018-12-03","cited_by_count":246},{"id":"2004.11791","title":"Federated learning with hierarchical clustering of local updates to improve training on non-IID data","created":"2020-04-24","cited_by_count":223},{"id":"2002.05516","title":"Federated Learning of a Mixture of Global and Local Models","created":"2020-02-10","cited_by_count":210},{"id":"1908.10530","title":"R\\'enyi Differential Privacy of the Sampled Gaussian Mechanism","created":"2019-08-28","cited_by_count":204},{"id":"1909.10594","title":"MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples","created":"2019-09-23","cited_by_count":197},{"id":"2108.10252","title":"Federated Multi-Task Learning under a Mixture of Distributions","created":"2021-08-23","cited_by_count":192},{"id":"2007.07646","title":"A Survey of Privacy Attacks in Machine Learning","created":"2020-07-15","cited_by_count":146},{"id":"2004.00010","title":"The Discrete Gaussian for Differential Privacy","created":"2020-03-31","cited_by_count":135},{"id":"2010.02372","title":"Lower Bounds and Optimal Algorithms for Personalized Federated Learning","created":"2020-10-05","cited_by_count":123},{"id":"1604.02390","title":"Minimax Optimal Procedures for Locally Private Estimation","created":"2016-04-08","cited_by_count":107},{"id":"2312.03262","title":"Low-Cost High-Power Membership Inference Attacks","created":"2023-12-06","cited_by_count":107},{"id":"2007.01162","title":"Tilted Empirical Risk Minimization","created":"2020-07-02","cited_by_count":106},{"id":"1812.06210","title":"A General Approach to Adding Differential Privacy to Iterative Training Procedures","created":"2018-12-15","cited_by_count":84},{"id":"1811.07971","title":"Private Selection from Private Candidates","created":"2018-11-19","cited_by_count":75},{"id":"2004.08856","title":"Local Differential Privacy based Federated Learning for Internet of Things","created":"2020-04-19","cited_by_count":75},{"id":"2111.09278","title":"Differentially Private Federated Learning on Heterogeneous Data","created":"2021-11-17","cited_by_count":74},{"id":"2009.06389","title":"Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy","created":"2020-09-10","cited_by_count":61},{"id":"2009.12562","title":"Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach","created":"2020-09-26","cited_by_count":53},{"id":"2202.08187","title":"Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey","created":"2022-02-16","cited_by_count":47},{"id":"2112.06053","title":"FedSoft: Soft Clustered Federated Learning with Proximal Local Updating","created":"2021-12-11","cited_by_count":46},{"id":"1901.09335","title":"Augment your batch: better training with larger batches","created":"2019-01-27","cited_by_count":37},{"id":"2305.01068","title":"Personalized Federated Learning under Mixture of Distributions","created":"2023-05-01","cited_by_count":37},{"id":"2206.07902","title":"On Privacy and Personalization in Cross-Silo Federated Learning","created":"2022-06-16","cited_by_count":36},{"id":"2206.07737","title":"Disparate Impact in Differential Privacy from Gradient Misalignment","created":"2022-06-15","cited_by_count":31},{"id":"2202.01666","title":"Proportional Fairness in Federated Learning","created":"2022-02-03","cited_by_count":21},{"id":"2002.12062","title":"Membership Inference Attacks and Defenses in Classification Models","created":"2020-02-27","cited_by_count":16},{"id":"2205.11584","title":"PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning","created":"2022-05-23","cited_by_count":13}],"next_cursor":null}