Federated Fine-Tuning of Video Segmentation Foundation Models with Shared Long-Term Memory Mechanisms
Keywords:
federated learning, video segmentation, foundation models, long-term memory, SAM2, edge computing, privacy, system architectureAbstract
The rapid advancement of video segmentation foundation models, exemplified by the Segment Anything paradigm and its temporally aware successors, has unlocked unprecedented capabilities for object-centric understanding across long video sequences. However, the massive computational requirements, sensitive nature of video data, and heterogeneous deployment environments call for a paradigm shift away from purely centralized training. This paper presents a federated fine-tuning framework in which geographically distributed clients collaboratively adapt a shared video segmentation model without exposing raw visual data. Central to the proposed system is a shared long-term memory mechanism that accumulates, consolidates, and propagates object appearance memory across clients, thereby preserving temporal coherence while adhering to strict privacy constraints. We explore the architectural design space, including memory coordination protocols, communication-computation trade-offs, and consistency models under non-IID video streams. The discussion extends to infrastructure requirements for scalable deployment at the edge, system-level robustness against data drift, fairness implications of aggregated memory representations, and governance challenges posed by cross-jurisdictional video analytics. By synthesizing perspectives from federated learning systems, memory-augmented neural architectures, and video understanding, the article articulates a comprehensive research agenda for privacy-preserving, memory-enhanced video segmentation that can operate reliably across large-scale, socio-technically complex environments.
References
1. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
2. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1-2), 1–210.
3. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., ... & Dollár, P. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
4. Ravi, N., Gabeur, V., Hu, Y.-T., Hu, R., Ryali, C., Ma, T., ... & Feichtenhofer, C. (2024). SAM 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714.
5. Graves, A., Wayne, G., & Danihelka, I. (2014). Neural Turing machines. arXiv preprint arXiv:1410.5401.
6. Weston, J., Chopra, S., & Bordes, A. (2014). Memory networks. arXiv preprint arXiv:1410.3916.
7. Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., & Salakhutdinov, R. (2019). Transformer-XL: Attentive language models beyond a fixed-length context. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 2978–2988).
8. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
9. Oh, S. W., Lee, J.-Y., Xu, N., & Kim, S. J. (2019). Video object segmentation using space-time memory networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9226–9235).
10. Wu, Y., Rabe, M. N., Li, D., Cho, J., & Le, Q. V. (2022). Memorizing transformers. arXiv preprint arXiv:2203.08913.
11. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60.
12. Li, G., Yuan, H., Chen, S., Hu, Q., Wang, J., & Jiang, K. (2026). MFT: Memory-Aware Fine-Tuning of SAM2 for Efficient Long-Sequence Video Object Segmentation. IEEE Signal Processing Letters.
13. Liu, Y., Huang, J., Li, W., Zhu, H., Li, B., & Wang, X. (2020). FedVision: An online visual object detection platform with federated learning. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1489–1497).
14. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., ... & Roselander, J. (2019). Towards federated learning at scale: System design. In Proceedings of the 2nd Conference on Machine Learning and Systems (MLSys).
15. Wang, J., Liu, Q., Liang, H., Joshi, G., & Poor, H. V. (2020). Tackling the objective inconsistency problem in heterogeneous federated optimization. In Advances in Neural Information Processing Systems (Vol. 33).
16. Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2020). SCAFFOLD: Stochastic controlled averaging for federated learning. In Proceedings of the 37th International Conference on Machine Learning (ICML).
17. Li, X., Huang, K., Yang, W., Wang, S., & Zhang, Z. (2020). On the convergence of federated optimization in heterogeneous networks. In Proceedings of the 3rd Conference on Machine Learning and Systems (MLSys).
18. Zhang, Y., Duan, T., Li, T., & Smith, V. (2021). FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization. In Advances in Neural Information Processing Systems (Vol. 34).
19. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018). Federated learning with non-iid data. arXiv preprint arXiv:1806.00582.
20. Wang, J., Zhang, Y., Li, Q., & Chen, M. (2021). Federated learning for video surveillance in smart cities: Challenges and opportunities. IEEE Internet of Things Journal, 8(23), 16961–16977.
21. Chen, M., Yang, Z., Saad, W., Yin, C., & Poor, H. V. (2021). Federated learning for edge networks: Resource optimization and incentive mechanism. IEEE Communications Magazine, 58(10), 50–56.
22. Zhang, B., Jin, X., Ratnasamy, S., Wawrzynek, J., & Lee, E. A. (2019). VideoEdge: Processing camera streams using hierarchical clusters of edge servers. In Proceedings of the 4th ACM/IEEE Symposium on Edge Computing (pp. 115–131).
23. Mohri, M., Sivek, G., & Suresh, A. T. (2019). Agnostic federated learning. In Proceedings of the 36th International Conference on Machine Learning (ICML).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
