Name
Title | ||
|---|---|---|
FedACS: Federated Skewness Analytics in Heterogeneous Decentralized Data Environments |
Abstract | ||
|---|---|---|
The emerging federated optimization paradigm performs data mining or artificial intelligence techniques locally on the edge devices, enabling scientists and engineers to utilize the blooming edge data with privacy protection. In such a paradigm, since data cannot be shared or gathered, data heterogeneity naturally emerges, which significantly degrades the performance of federated optimization, ult... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IWQOS52092.2021.9521301 | 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) |
Keywords | DocType | ISSN |
federated analytics,data heterogeneity,federated learning,dueling bandit | Conference | 1548-615X |
ISBN | Citations | PageRank |
978-1-6654-1494-4 | 0 | 0.34 |
References | Authors | |
0 | 4 | |