Secure Aggregation with Heterogeneous Quantization in Federated Learning

By Ahmed Roushdy Elkordy, Amir Salman Avestimehr. 2020

29 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. Qinbin Li
8. Praneeth Vepakomma

Citations in the corpus

• FedML: A Research Library and Benchmark for Federated Machine Learning (2020, positive): “To tackle the challenge of system constraints, researchers apply sparsification and quantization techniques to reduce the communication overheads and computation costs during the training process [6, 7, 8, 9, 10, 11, 12, 13, 14].”
• Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges (2020, background): “In another work [50], the authors proposed a secure aggregation scheme using heterogeneous quantization for federated learning.”