Transaction

7b375c82f8554aa0c1dfd5a4a560e2d4e6a175ddbc587201ec51f767ef8f52b4
Timestamp (utc)
2024-05-28 07:08:58
Fee Paid
0.00000004 BSV
(
0.00330423 BSV
-
0.00330419 BSV
)
Fee Rate
2.347 sat/KB
Version
1
Confirmations
88,777
Size Stats
1,704 B

3 Outputs

Total Output:
0.00330419 BSV
  • jmetaB0248d6aa259c9e9284ece509e4e3452fa5e333de92dcd0354cc5e27a23ae5bedbb@f9851c2160f6d07cecd53064b44b75ae180251462771b97e11dbfad2c8f6cbe5rss.item metarss.netM<item> <title>GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning</title> <link>https://arxiv.org/abs/2403.17833</link> <description>arXiv:2403.17833v2 Announce Type: replace-cross Abstract: Federated learning client selection is crucial for determining participant clients while balancing model accuracy and communication efficiency. Existing methods have limitations in handling data heterogeneity, computational burdens, and independent client treatment. To address these challenges, we propose GPFL, which measures client value by comparing local and global descent directions. We also employ an Exploit-Explore mechanism to enhance performance. Experimental results on FEMINST and CIFAR-10 datasets demonstrate that GPFL outperforms baselines in Non-IID scenarios, achieving over 9\% improvement in FEMINST test accuracy. Moreover, GPFL exhibits shorter computation times through pre-selection and parameter reuse in federated learning.</description> <guid isPermaLink="false">oai:arXiv.org:2403.17833v2</guid> <category>cs.LG</category> <category>cs.DC</category> <arxiv:announce_type>replace-cross</arxiv:announce_type> <dc:rights>http://creativecommons.org/licenses/by/4.0/</dc:rights> <dc:creator>Shijie Na, Yuzhi Liang, Siu-Ming Yiu</dc:creator> </item>
    https://whatsonchain.com/tx/7b375c82f8554aa0c1dfd5a4a560e2d4e6a175ddbc587201ec51f767ef8f52b4