{"list":"other","total":52,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"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":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"2007.07481","title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization","created":"2020-07-15","cited_by_count":906},{"id":"1812.01097","title":"LEAF: A Benchmark for Federated Settings","created":"2018-12-03","cited_by_count":792},{"id":"1807.00459","title":"How To Backdoor Federated Learning","created":"2018-07-02","cited_by_count":765},{"id":"1803.01498","title":"Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates","created":"2018-03-05","cited_by_count":711},{"id":"2002.06440","title":"Federated Learning with Matched Averaging","created":"2020-02-15","cited_by_count":564},{"id":"1805.04049","title":"Exploiting Unintended Feature Leakage in Collaborative Learning","created":"2018-05-10","cited_by_count":520},{"id":"1712.01887","title":"Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training","created":"2017-12-05","cited_by_count":478},{"id":"1712.07557","title":"Differentially Private Federated Learning: A Client Level Perspective","created":"2017-12-20","cited_by_count":459},{"id":"1907.09693","title":"A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection","created":"2019-07-23","cited_by_count":374},{"id":"1901.03407","title":"Deep Learning for Anomaly Detection: A Survey","created":"2019-01-10","cited_by_count":358},{"id":"1811.12470","title":"Analyzing Federated Learning through an Adversarial Lens","created":"2018-11-29","cited_by_count":351},{"id":"1702.07464","title":"Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning","created":"2017-02-24","cited_by_count":334},{"id":"2007.13518","title":"FedML: A Research Library and Benchmark for Federated Machine Learning","created":"2020-07-27","cited_by_count":300},{"id":"1812.00535","title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning","created":"2018-12-03","cited_by_count":246},{"id":"1607.00148","title":"LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection","created":"2016-07-01","cited_by_count":215},{"id":"1812.03224","title":"A Hybrid Approach to Privacy-Preserving Federated Learning","created":"2018-12-07","cited_by_count":210},{"id":"1710.09854","title":"Gradient Sparsification for Communication-Efficient Distributed Optimization","created":"2017-10-26","cited_by_count":201},{"id":"1611.04482","title":"Practical Secure Aggregation for Federated Learning on User-Held Data","created":"2016-11-14","cited_by_count":178},{"id":"1808.04866","title":"Mitigating Sybils in Federated Learning Poisoning","created":"2018-08-14","cited_by_count":161},{"id":"2007.01154","title":"Federated Learning with Compression: Unified Analysis and Sharp Guarantees","created":"2020-07-02","cited_by_count":160},{"id":"1811.04017","title":"A generic framework for privacy preserving deep learning","created":"2018-11-09","cited_by_count":103},{"id":"1609.08326","title":"Asynchronous Stochastic Gradient Descent with Delay Compensation","created":"2016-09-27","cited_by_count":99},{"id":"1804.07474","title":"D\\\"IoT: A Federated Self-learning Anomaly Detection System for IoT","created":"2018-04-20","cited_by_count":89},{"id":"1803.06443","title":"Communication Compression for Decentralized Training","created":"2018-03-17","cited_by_count":88},{"id":"1610.05202","title":"Decentralized Collaborative Learning of Personalized Models over Networks","created":"2016-10-17","cited_by_count":76},{"id":"2004.10397","title":"A Framework for Evaluating Gradient Leakage Attacks in Federated Learning","created":"2020-04-22","cited_by_count":64},{"id":"1911.10071","title":"Federated Learning with Bayesian Differential Privacy","created":"2019-11-22","cited_by_count":60},{"id":"2007.09712","title":"Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach","created":"2020-07-19","cited_by_count":56},{"id":"2006.10672","title":"Federated Learning With Quantized Global Model Updates","created":"2020-06-18","cited_by_count":55},{"id":"1911.04206","title":"Practical Federated Gradient Boosting Decision Trees","created":"2019-11-11","cited_by_count":54},{"id":"1912.05897","title":"HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning","created":"2019-12-12","cited_by_count":51},{"id":"1801.10228","title":"Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains","created":"2018-01-30","cited_by_count":49},{"id":"2010.12229","title":"Throughput-Optimal Topology Design for Cross-Silo Federated Learning","created":"2020-10-23","cited_by_count":39},{"id":"2002.09692","title":"Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection","created":"2020-02-22","cited_by_count":20},{"id":"2010.07427","title":"BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture","created":"2020-10-14","cited_by_count":20},{"id":"1907.10147","title":"A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices","created":"2019-07-23","cited_by_count":18},{"id":"1910.08385","title":"Federated Generative Privacy","created":"2019-10-18","cited_by_count":16},{"id":"2006.07026","title":"Backdoor Attacks on Federated Meta-Learning","created":"2020-06-12","cited_by_count":15},{"id":"2001.09697","title":"Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process","created":"2020-01-27","cited_by_count":7},{"id":"1707.01873","title":"Blockchain Consensus Protocols in the Wild","created":"2017-07-06","cited_by_count":6},{"id":"2012.06974","title":"Federated Mimic Learning for Privacy Preserving Intrusion Detection","created":"2020-12-13","cited_by_count":6},{"id":"1905.00919","title":"Mimic Learning to Generate a Shareable Network Intrusion Detection Model","created":"2019-05-02","cited_by_count":3},{"id":"2103.01116","title":"Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles","created":"2021-03-01","cited_by_count":3}],"next_cursor":null}