FL@FM 2024 Workshop Series Edited Book at LNAI
"Federated Learning in the Age of Foundation Models"


Chapter Camera Ready Due: October 15, 2024 (23:59:59 AoE)
Admin Check Feedback: October 22, 2024 (23:59:59 AoE)
Revision Due (if required): November 01, 2024 (23:59:59 AoE)

Edited Book

Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). (2025). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, p. 178. Springer, Cham.

Chapters

  1. Guo, Z., Zhang, Y., Zhang, Z., Xu, Z. & King, I. (2025). Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 1-16. Springer, Cham.
  2. Lu, Q., Yu, H., Wang, J., Teney, D., Wang, H., Zhu, Y., Chen, Y., Yang, Q., Xie, X. & Ji, X. (2025). ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 17-32. Springer, Cham.
  3. Roth, H., Beutel, D., Cheng, Y., Marques, J. F., Pan, H., Chen, C., Zhang, Z., Wen, Y., Yang, S., Yang, I. T.-C., Hsieh, Y.-T., Xu, Z., Xu, D., Lane, N. D. & Feng, A. (2025). Supercharging Federated Learning with Flower and NVIDIA FLARE. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 33-42. Springer, Cham.
  4. Chen, S., You, L., Liu, R., Yu, S. & Abdelmoniem, A. M. (2025). Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-constrained IoT Clients. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 43-58. Springer, Cham.
  5. Yan, X., Wang, Z. & Jin, Y. (2025). Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 59-74. Springer, Cham.
  6. Li, Z., Wu, X., Tang, X., He, T., Ong, Y.-S., Chen, M., Liu, Q., Lao, Q. & Yu, H. (2025). Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 75-90. Springer, Cham.
  7. Yu, Y., Yan, Y., Cai, J. & Jin, Y. (2025). Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 91-103. Springer, Cham.
  8. Abacha, F., Teo, S. G., Cordeiro, L. & Mustafa, M. (2025). Synthetic Data Aided Federated Learning Using Foundation Models. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 104-116. Springer, Cham.
  9. Ye, R., Ge, R., Yuchi, F., Chai, J., Wang, Y. & Chen, S. (2025). Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 117-130. Springer, Cham.
  10. Zhang, Z., Hu, X., Zhang, J., Zhang, Y., Wang, H., Qu, L. & Xu, Z. (2025). FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 131-146. Springer, Cham.
  11. Yan, H. & Guo, Y. (2025). Lightweight Unsupervised Federated Learning. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 147-162. Springer, Cham.
  12. Guo, X., Yi, L., Wu, X., Yu, K. & Wang, G. (2025). Enhancing Causal Discovery in Federated Settings with Limited Local Samples. In: Yu, H., Li, X., Xu, Z., Goebel, R. & King, I. (Eds.). Federated Learning in the Age of Foundation Models. Lecture Notes in Artificial Intelligence, vol. 15501, pp. 163-178. Springer, Cham.

Instructions for Authors

   

Selected papers from the FL@FM 2024 workshop series (FL@FM-TheWebConf'24, FL@FM-ICME'24, FL@FM-IJCAI'24 and FL@FM-NeurIPS'24) have been invited for publication as book chapters in the Lecture Notes in Artificial Intelligence (LNAI) - Federated Learning in the Age of Foundation Models.

Authors should consult Springer's authors' instructions and use the proceedings templates for LaTeX to prepare the chapters. 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 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 FL@FM-IJCAI'24 easychair submission site: https://easychair.org/conferences/?conf=flfm-ijcai-24. If you are an existing FL@FM-IJCAI'24 author, instead of creating a new submission, please re-use the same submission for your FL@FM-IJCAI'24 paper to upload your book chapter file. Please upload both the PDF file of the chapter as well as a zip file containing all the Latex source files you use to compile the PDF file. Your License-to-Publish form should also be included in the zip file.

For enquiries, please email us: flfm-ijcai-24@easychair.org


Editorial Team