Jakub Konečný
Publications by Year
Research Areas
Stochastic Gradient Optimization Techniques, Privacy-Preserving Technologies in Data, Sparse and Compressive Sensing Techniques, Cryptography and Data Security, Complexity and Algorithms in Graphs
Most-Cited Works
- → Advances and Open Problems in Federated Learning(2020)4,363 cited
- → Federated Learning: Strategies for Improving Communication Efficiency(2016)3,046 cited
- → Federated Optimization: Distributed Machine Learning for On-Device Intelligence(2016)1,652 cited
- → Towards Federated Learning at Scale: System Design(2019)954 cited
- → Federated Optimization:Distributed Optimization Beyond the Datacenter(2015)580 cited
- → Improving Federated Learning Personalization via Model Agnostic Meta Learning(2019)368 cited
- → LEAF: A Benchmark for Federated Settings(2018)285 cited
- → Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting(2015)263 cited
- → Distributed optimization with arbitrary local solvers(2017)195 cited
- → A Field Guide to Federated Optimization(2021)167 cited