Maverick Matters: Client Contribution and Selection in Federated Learning

Maverick Matters: Client Contribution and Selection in Federated Learning. Huang, J., Hong, C., Liu, Y., Chen, L. Y., & Roos, S. In Kashima, H., Idé, T., & Peng, W., editors, Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part II, volume 13936, of Lecture Notes in Computer Science, pages 269–282, 2023. Springer.

Paper doi bibtex

@inproceedings{DBLP:conf/pakdd/HuangHLCR23,
  author       = {Jiyue Huang and
                  Chi Hong and
                  Yang Liu and
                  Lydia Y. Chen and
                  Stefanie Roos},
  editor       = {Hisashi Kashima and
                  Tsuyoshi Id{\'{e}} and
                  Wen{-}Chih Peng},
  title        = {Maverick Matters: Client Contribution and Selection in Federated Learning},
  booktitle    = {Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia
                  Conference on Knowledge Discovery and Data Mining, {PAKDD} 2023, Osaka,
                  Japan, May 25-28, 2023, Proceedings, Part {II}},
  series       = {Lecture Notes in Computer Science},
  volume       = {13936},
  pages        = {269--282},
  publisher    = {Springer},
  year         = {2023},
  url          = {https://doi.org/10.1007/978-3-031-33377-4\_21},
  doi          = {10.1007/978-3-031-33377-4\_21},
  timestamp    = {Tue, 12 Sep 2023 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/pakdd/HuangHLCR23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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