Artemis: tight convergence guarantees for bidirectional compression in Federated Learning

By Constantin Philippenko, Aymeric Dieuleveut. 2020

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chaoyang He
2. Yang Liu
3. Murali Annavaram
4. Qiang Yang
5. Jinhyun So
6. Abhishek Singh
7. Praneeth Vepakomma
8. Qinbin Li

Citations in the corpus

• FedML: A Research Library and Benchmark for Federated Machine Learning (2020, methodology): “To tackle the system constraints challenge, researchers apply compression, sparsification, or quantization techniques to reduce the communication overheads and computation costs during the training process [6, 7, 8, 9, 10, 11, 12].”