Aug 2026· Machine-mediated learning· Vol 115· 0 citations· 105 references
TL;DR
This survey provides a comprehensive overview of existing research at the intersection of LLMs and FL, and outlines promising future research directions to advance the scalability, efficiency, and ethical deployment of LLMs in federated settings, paving the way for more trustworthy and privacy-preserving natural language processing systems.
A comprehensive survey of federated prompt learning (FPL) is presented to review recent advances in integrating the federated learning paradigm and large language models, while discussing security, privacy, and robustness issues and summarizing existing defense mechanisms.
Qinglin Yang, Chen Qiu, Hongyu Zhang et al.· 0 citations
A review of federated learning through a structured taxonomy that covers its core architectural paradigms, major learning types, model training approaches, and aggregation mechanisms, and analyzes the principal challenges confronting FL, including privacy and security risks, statistical and system heterogeneity, commun...
Mahdiyeh Velaei, Hosna Ghahramani, Ali Ghaffari et al.· Cluster Computing· 0 citations
A comprehensive survey of FedTTA is provided, formalizing its problem setting and establishing a unified taxonomy encompassing three paradigms: i) Federated Initialization and Test Fine-tuning, where the global model serves as a robust prior for local refinement; ii) Federated Shared Backbone and Test Personalized Adap...
Chen Zhang, Ge Su, Huaxia Zhou et al.· International Journal of Com...· 0 citations
The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models brings together researchers and practitioners from data mining, machine learning, security, and responsible AI to address issues from a data-centric, system-level perspective.
Lu Lin, Jinghui Chen, Ting Wang et al.· Proceedings of the 32nd ACM...· 0 citations
A unified online log anomaly detection framework, FLMLog, which is based on federated Learning and large language model, and adopts a prefix-aware in-context learning (ICL) refinement strategy, which achieves an improvement in prefix caching efficiency.
Shuai Xu· Poster Volume 0008 The 2026...· 0 citations
: Large language models are increasingly being deployed in safety-critical domains, yet remain vulnerable to jailbreak attacks that circumvent safety alignments. This systematic review synthesizes empirical jailbreak research published between 2024 and 2025, using a PRISMA-guided search protocol, followed by BERTopic-b...
Meda Račaitytė, Hélder Bastos, R. Ribeiro et al.· International Conference on...· 0 citations
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