FL-IJCAI'22 Post-Workshop Publications at LNAI
- Trustworthy Federated Learning


Submission Due: September 26, 2022 (23:59:59 AoE)
First Round Review Notification: October 24, 2022 (23:59:59 AoE)
Revised Paper Due (if revisions are required): November 07, 2022 (23:59:59 AoE)
Final Notification: November 25, 2022 (23:59:59 AoE)
Camera Ready Due: December 12, 2022 (23:59:59 AoE)

Edited Book

Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). (2023). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, p. 160. Springer, Cham.

Chapters

  1. Isaksson, M., Zec, E. L., Cöster, R., Gillblad, D. & Girdzijauskas, S. (2023). Adaptive Expert Models for Personalization in Federated Learning. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 1-16. Springer, Cham.
  2. Li, Z., Shao, J., Mao, Y., Wang, J. H. & Zhang, J. (2023). Federated Learning with GAN-based Data Synthesis for Non-IID Clients. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 17-32. Springer, Cham.
  3. Yang, I., Zhang, J., Chai, D., Wang, L., Guo, K., Chen, K. & Yang, Q. (2023). Practical and Secure Federated Recommendation with Personalized Mask. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 33-45. Springer, Cham.
  4. Fraboni, Y., Vidal, R., Kameni, L. & Lorenzi, M. (2023). A General Theory for Client Sampling in Federated Learning. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 46-59. Springer, Cham.
  5. Zec, E. L., Ekblom, E., Willbo, M., Mogren, O. & Girdzijauskas, S. (2023). Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 60-72. Springer, Cham.
  6. Kollias, G., Salonidis, T. & Wang, S. (2023). Sketch to Skip and Select: Communication Efficient Federated Learning using Locality Sensitive Hashing. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 73-84. Springer, Cham.
  7. Verardo, G., Barreira, D., Chiesa, M., Kostic, D. & Maguire, G. Q. J. (2023). Fast Server Learning Rate Tuning for Coded Federated Dropout. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 85-100. Springer, Cham.
  8. Hoech, H., Rischke, R., Müller, K. & Samek, W. (2023). FedAUXfdp: Differentially Private One-Shot Federated Distillation. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 101-115. Springer, Cham.
  9. Cai, S., Chai, D., Yang, L., Zhang, J., Jin, Y., Wang, L., Guo, K. & Chen, K. (2023). Secure Forward Aggregation for Vertical Federated Neural Networks. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 116-130. Springer, Cham.
  10. Zhao, S., Liu, J., Ma, G., Yang, J., Liu, D. & Li, Z. (2023). Two-phased Federated Learning with Cluster-based Personalization for Natural Gas Load Forecasting. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 131-144. Springer, Cham.
  11. Cai, J., Liu, Y., Liu, X., Li, J. & Zhuang, H. (2023). Privacy-Preserving Federated Cross-Domain Social Recommendation. In: Goebel, R., Yu, H., Faltings, B., Fan, L. & Xiong, Z. (Eds.). Trustworthy Federated Learning. Lecture Notes in Artificial Intelligence, vol. 13448, pp. 145-160. Springer, Cham.

Instructions for Authors

   

All accepted workshop papers are invited to be extended and re-reviewed for publication as book chapters in the Lecture Notes in Artificial Intelligence (LNAI) - Trustworthy Federated Learning.

Authors should consult Springer's authors' instructions and use the proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a License-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.

Page limit: up to 16 pages including figures and references, etc.

If you or any of the authors contributing to FL 2022 are interested in Open Access or Open Choice, please refer to our webpage for prices and additional information. We would need the invoicing address and the CC-BY licence-to-publish agreement at the same time as the files for the publication.

We will continue to use the same easychair submission site: https://easychair.org/conferences/?conf=fl-ijcai-22. Instead of creating a new submission, please just use the same submission for your FL-IJCAI'22 paper to upload your book chapter file.

For enquiries, please email us: fl-ijcai-22@easychair.org


Editorial Team