Large Language Models (LLMs) have revolutionized natural language processing, yet their deployment is hindered by data, computation, and privacy constraints. Federated Learning (FL) offers a promising solution by enabling collaborative, privacy-preserving training across distributed devices, while the push for low-latency on-device intelligence further drives LLM integration into FL and edge settings—posing new challenges in heterogeneity and resource limits. This survey comprehensively reviews the integration of LLMs with federated learning, termed FLM, and its deployment at the edge, with particular emphasis on the robustness, privacy, and trustworthiness challenges that emerge across th... More >

With the rapid growth of Internet of Things (IoT) applications and decentralized data-driven systems, secure and flexible data sharing remains a critical challenge. Identity-Based Proxy Re-Encryption (IB-PRE) is an effective cryptographic primitive for enabling fine-grained access delegation without exposing plaintext data. However, existing IB-PRE schemes remain vulnerable to algorithm substitution attacks (ASA), malicious key generation, inefficient ciphertext management, and the lack of practical revocation mechanisms. To address these limitations, we propose a blockchain-based revocable identity-based proxy re-encryption scheme with cryptographic reverse firewalls (BRIBPR-CRF) for secure... More >