{"list":"other","total":61,"rows":[{"id":"1602.05629","title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","created":"2016-02-17","cited_by_count":7669},{"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":"1609.04747","title":"An overview of gradient descent optimization algorithms","created":"2016-09-15","cited_by_count":1033},{"id":"2003.08119","title":"The Future of Digital Health with Federated Learning","created":"2020-03-18","cited_by_count":633},{"id":"2002.06440","title":"Federated Learning with Matched Averaging","created":"2020-02-15","cited_by_count":564},{"id":"1712.01887","title":"Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training","created":"2017-12-05","cited_by_count":478},{"id":"1909.11875","title":"Federated Learning in Mobile Edge Networks: A Comprehensive Survey","created":"2019-09-26","cited_by_count":408},{"id":"1812.00564","title":"Split learning for health: Distributed deep learning without sharing raw patient data","created":"2018-12-03","cited_by_count":395},{"id":"1909.13014","title":"FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization","created":"2019-09-28","cited_by_count":356},{"id":"1804.08333","title":"Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge","created":"2018-04-23","cited_by_count":352},{"id":"1702.07464","title":"Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning","created":"2017-02-24","cited_by_count":334},{"id":"1812.11750","title":"Federated Learning via Over-the-Air Computation","created":"2018-12-31","cited_by_count":219},{"id":"1812.11494","title":"Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)","created":"2018-12-30","cited_by_count":191},{"id":"1907.09173","title":"FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare","created":"2019-07-22","cited_by_count":182},{"id":"1810.04304","title":"Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation","created":"2018-10-10","cited_by_count":149},{"id":"2009.13012","title":"Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges","created":"2020-09-28","cited_by_count":139},{"id":"1908.06287","title":"Scheduling Policies for Federated Learning in Wireless Networks","created":"2019-08-17","cited_by_count":131},{"id":"1908.06847","title":"Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges","created":"2019-07-30","cited_by_count":129},{"id":"2104.02151","title":"Distributed Learning in Wireless Networks: Recent Progress and Future Challenges","created":"2021-04-05","cited_by_count":129},{"id":"1803.04311","title":"Deep Learning in Mobile and Wireless Networking: A Survey","created":"2018-03-12","cited_by_count":118},{"id":"1903.07424","title":"Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation","created":"2019-03-18","cited_by_count":100},{"id":"1905.05957","title":"DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression","created":"2019-05-15","cited_by_count":99},{"id":"1909.02362","title":"Hierarchical Federated Learning Across Heterogeneous Cellular Networks","created":"2019-09-05","cited_by_count":98},{"id":"1910.06837","title":"Reliable Federated Learning for Mobile Networks","created":"2019-10-14","cited_by_count":92},{"id":"1910.01355","title":"SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead","created":"2019-10-03","cited_by_count":82},{"id":"1805.08768","title":"Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication","created":"2018-05-22","cited_by_count":51},{"id":"2103.17150","title":"Federated Learning: A Signal Processing Perspective","created":"2021-03-31","cited_by_count":49},{"id":"2006.02499","title":"Wireless Communications for Collaborative Federated Learning","created":"2020-06-03","cited_by_count":47},{"id":"1909.00907","title":"Energy Demand Prediction with Federated Learning for Electric Vehicle Networks","created":"2019-09-03","cited_by_count":46},{"id":"2006.14591","title":"Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees","created":"2020-06-25","cited_by_count":46},{"id":"1910.14648","title":"Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks","created":"2019-10-31","cited_by_count":39},{"id":"2011.05051","title":"Federated Learning via Intelligent Reflecting Surface","created":"2020-11-10","cited_by_count":38},{"id":"1905.00152","title":"Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network","created":"2019-05-01","cited_by_count":37},{"id":"2006.02931","title":"Federated Learning for 6G Communications: Challenges, Methods, and Future Directions","created":"2020-06-04","cited_by_count":33},{"id":"1805.06591","title":"Deep Reinforcement Learning for Resource Management in Network Slicing","created":"2018-05-17","cited_by_count":27},{"id":"2104.03008","title":"FedFace: Collaborative Learning of Face Recognition Model","created":"2021-04-07","cited_by_count":26},{"id":"1909.12567","title":"Cell-Free Massive MIMO for Wireless Federated Learning","created":"2019-09-27","cited_by_count":25},{"id":"2007.03970","title":"Distributed Training of Deep Learning Models: A Taxonomic Perspective","created":"2020-07-08","cited_by_count":25},{"id":"2008.10846","title":"Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO","created":"2020-08-25","cited_by_count":23},{"id":"2012.01489","title":"Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications","created":"2020-12-02","cited_by_count":21},{"id":"2103.08870","title":"Learned Gradient Compression for Distributed Deep Learning","created":"2021-03-16","cited_by_count":20},{"id":"2105.11028","title":"Fast Federated Learning by Balancing Communication Trade-Offs","created":"2021-05-23","cited_by_count":18},{"id":"2102.02802","title":"Federated mmWave Beam Selection Utilizing LIDAR Data","created":"2021-02-04","cited_by_count":12},{"id":"2103.11870","title":"A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning","created":"2021-03-22","cited_by_count":11},{"id":"1901.00844","title":"Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air","created":"2019-01-03","cited_by_count":10},{"id":"2004.11361","title":"Enhancing Privacy via Hierarchical Federated Learning","created":"2020-04-23","cited_by_count":10},{"id":"2101.03627","title":"Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks","created":"2021-01-10","cited_by_count":10},{"id":"2012.15831","title":"Timely Communication in Federated Learning","created":"2020-12-31","cited_by_count":9},{"id":"1906.04594","title":"Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing","created":"2019-06-10","cited_by_count":6},{"id":"1908.03242","title":"Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics","created":"2019-08-08","cited_by_count":6},{"id":"1904.12475","title":"Over-the-Air Computation via Intelligent Reflecting Surfaces","created":"2019-04-29","cited_by_count":5},{"id":"2004.09168","title":"Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems","created":"2020-04-20","cited_by_count":5},{"id":"2011.07271","title":"FedRec: Federated Learning of Universal Receivers over Fading Channels","created":"2020-11-14","cited_by_count":5},{"id":"2008.02609","title":"On the relationship between (secure) multi-party computation and (secure) federated learning","created":"2020-08-06","cited_by_count":4},{"id":"2008.13492","title":"Wireless for Machine Learning","created":"2020-08-31","cited_by_count":4},{"id":"2104.06990","title":"Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges","created":"2021-04-14","cited_by_count":4},{"id":"2008.05189","title":"Dispersed Federated Learning: Vision, Taxonomy, and Future Directions","created":"2020-08-12","cited_by_count":3},{"id":"2007.06134","title":"Adaptive Periodic Averaging: A Practical Approach to Reducing Communication in Distributed Learning","created":"2020-07-13","cited_by_count":2},{"id":"2101.07032","title":"Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks","created":"2021-01-18","cited_by_count":2},{"id":"1412.8109","title":"Complex support vector machines regression for robust channel estimation in LTE downlink system","created":"2014-12-10","cited_by_count":1}],"next_cursor":null}