C2S: Class-aware client selection for effective aggregation in federated learning
Federated learning is proposed to train distributed data in a safe manner by avoiding to send data to server. The server maintains a global model and sends it to clients in each communication round, and then aggregates the updated local models to derive a new global model. Traditionally, the clients...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-09-01
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| Series: | High-Confidence Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667295222000204 |