{"list":"other","total":70,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2204.06125","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","created":"2022-04-13","cited_by_count":6585},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1912.04977","title":"Advances and Open Problems in Federated Learning","created":"2019-12-10","cited_by_count":3046},{"id":"1812.06127","title":"Federated Optimization in Heterogeneous Networks","created":"2018-12-14","cited_by_count":2987},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1908.07873","title":"Federated Learning: Challenges, Methods, and Future Directions","created":"2019-08-21","cited_by_count":1538},{"id":"1907.02189","title":"On the Convergence of FedAvg on Non-IID Data","created":"2019-07-04","cited_by_count":1229},{"id":"1806.00582","title":"Federated Learning with Non-IID Data","created":"2018-06-02","cited_by_count":1108},{"id":"2003.00295","title":"Adaptive Federated Optimization","created":"2020-02-29","cited_by_count":1046},{"id":"1909.06335","title":"Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification","created":"2019-09-13","cited_by_count":851},{"id":"1707.08114","title":"A Survey on Multi-Task Learning","created":"2017-07-25","cited_by_count":840},{"id":"1812.01097","title":"LEAF: A Benchmark for Federated Settings","created":"2018-12-03","cited_by_count":792},{"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":"1712.07557","title":"Differentially Private Federated Learning: A Client Level Perspective","created":"2017-12-20","cited_by_count":459},{"id":"2003.13461","title":"Adaptive Personalized Federated Learning","created":"2020-03-30","cited_by_count":358},{"id":"1605.02065","title":"Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds","created":"2016-05-06","cited_by_count":346},{"id":"2104.07586","title":"See through Gradients: Image Batch Recovery via GradInversion","created":"2021-04-15","cited_by_count":326},{"id":"2001.01523","title":"Think Locally, Act Globally: Federated Learning with Local and Global Representations","created":"2020-01-06","cited_by_count":325},{"id":"2002.10619","title":"Three Approaches for Personalization with Applications to Federated Learning","created":"2020-02-25","cited_by_count":325},{"id":"1909.12488","title":"Improving Federated Learning Personalization via Model Agnostic Meta Learning","created":"2019-09-27","cited_by_count":269},{"id":"1905.03871","title":"Differentially Private Learning with Adaptive Clipping","created":"2019-05-09","cited_by_count":220},{"id":"2103.04628","title":"Personalized Federated Learning using Hypernetworks","created":"2021-03-08","cited_by_count":212},{"id":"1812.03224","title":"A Hybrid Approach to Privacy-Preserving Federated Learning","created":"2018-12-07","cited_by_count":210},{"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":"2103.00039","title":"Practical and Private (Deep) Learning without Sampling or Shuffling","created":"2021-02-26","cited_by_count":170},{"id":"1811.12469","title":"Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity","created":"2018-11-29","cited_by_count":164},{"id":"1910.10252","title":"Federated Evaluation of On-device Personalization","created":"2019-10-22","cited_by_count":147},{"id":"2002.04758","title":"Salvaging Federated Learning by Local Adaptation","created":"2020-02-12","cited_by_count":142},{"id":"2004.00010","title":"The Discrete Gaussian for Differential Privacy","created":"2020-03-31","cited_by_count":135},{"id":"2102.06387","title":"The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation","created":"2021-02-12","cited_by_count":128},{"id":"2010.02372","title":"Lower Bounds and Optimal Algorithms for Personalized Federated Learning","created":"2020-10-05","cited_by_count":123},{"id":"1808.01394","title":"Distributed Differential Privacy via Shuffling","created":"2018-08-04","cited_by_count":115},{"id":"2110.03620","title":"Hyperparameter Tuning with Renyi Differential Privacy","created":"2021-10-07","cited_by_count":104},{"id":"1412.6651","title":"Deep learning with Elastic Averaging SGD","created":"2014-12-20","cited_by_count":90},{"id":"1812.06210","title":"A General Approach to Adding Differential Privacy to Iterative Training Procedures","created":"2018-12-15","cited_by_count":84},{"id":"1908.07643","title":"AdaCliP: Adaptive Clipping for Private SGD","created":"2019-08-20","cited_by_count":77},{"id":"1811.07971","title":"Private Selection from Private Candidates","created":"2018-11-19","cited_by_count":75},{"id":"2202.00580","title":"Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification","created":"2022-02-01","cited_by_count":73},{"id":"2102.11845","title":"Learning with User-Level Privacy","created":"2021-02-23","cited_by_count":72},{"id":"1904.02200","title":"Differentially Private Model Publishing for Deep Learning","created":"2019-04-03","cited_by_count":71},{"id":"2009.10031","title":"Training Production Language Models without Memorizing User Data","created":"2020-09-21","cited_by_count":68},{"id":"2110.04995","title":"The Skellam Mechanism for Differentially Private Federated Learning","created":"2021-10-11","cited_by_count":68},{"id":"2010.09063","title":"Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization","created":"2020-10-18","cited_by_count":58},{"id":"1909.05830","title":"Differentially Private Meta-Learning","created":"2019-09-12","cited_by_count":41},{"id":"2203.03761","title":"The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning","created":"2022-03-07","cited_by_count":39},{"id":"1504.06998","title":"Heterogeneous Differential Privacy","created":"2015-04-27","cited_by_count":30},{"id":"2106.09779","title":"Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses","created":"2021-06-17","cited_by_count":30},{"id":"2102.11158","title":"Federated $f$-Differential Privacy","created":"2021-02-22","cited_by_count":29},{"id":"2003.12880","title":"Federated Residual Learning","created":"2020-03-28","cited_by_count":23},{"id":"2109.08119","title":"Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer","created":"2021-09-16","cited_by_count":23},{"id":"2010.12603","title":"Permute-and-Flip: A new mechanism for differentially private selection","created":"2020-10-23","cited_by_count":22},{"id":"1812.02890","title":"Three Tools for Practical Differential Privacy","created":"2018-12-07","cited_by_count":20},{"id":"2107.11839","title":"Differential Privacy in the Shuffle Model: A Survey of Separations","created":"2021-07-25","cited_by_count":15},{"id":"2108.12978","title":"Private Multi-Task Learning: Formulation and Applications to Federated Learning","created":"2021-08-30","cited_by_count":11},{"id":"2107.08371","title":"An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging","created":"2021-07-18","cited_by_count":10},{"id":"1612.04022","title":"Distributed Multi-Task Relationship Learning","created":"2016-12-13","cited_by_count":7},{"id":"1702.04423","title":"Efficient Multitask Feature and Relationship Learning","created":"2017-02-14","cited_by_count":7},{"id":"2007.05553","title":"Differentially private cross-silo federated learning","created":"2020-07-10","cited_by_count":7},{"id":"1510.08204","title":"Global Games with Noisy Information Sharing","created":"2015-10-28","cited_by_count":1},{"id":"2103.07491","title":"Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions","created":"2021-03-12","cited_by_count":1}],"next_cursor":null}