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 development of edge computing and intelligent maritime transportation systems, secure authentication and key agreement protocols have become essential for protecting communications among maritime entities and edge devices. Recently, Mahmood et al. proposed an authentication protocol for edge-centric maritime transportation systems, claiming that their scheme can resist various security attacks while ensuring efficient communication. However, practical maritime environments still face many security threats. In addition, edge infrastructures usually have limited resources. Therefore, authentication protocols require rigorous security evaluation. In this paper, we perform a crypt... More >

Blockchain technology offers transformative potential across distributed computing domains; however, dominant consensus mechanisms such as Proof of Work (PoW) impose severe energy and carbon costs, and existing research addresses only isolated components—consensus, storage, or sharding—rather than the full architectural system. This paper introduces the Energy-Optimized Blockchain Architecture (EOBA), the first nine-layer framework in which energy awareness is a foundational, cross-cutting design principle embedded across all architectural layers. EOBA integrates energy monitoring, hybrid energy-aware consensus, energy-guided task scheduling, dynamic sharding, hybrid on-chain/off-chain s... More >

In the context of smart cities, sensors are pivotal for the collection and analysis of health data within smart healthcare systems. Despite their importance, securing this data for cloud storage poses significant challenges, particularly in terms of data searchability and encryption. Our paper introduces a novel solution, namely certificateless encryption supporting equality test with cryptographic reverse firewalls (CLE-ET-CRF). This protocol allows cloud servers to perform equality tests on encrypted data without compromising its confidentiality, effectively mitigating risks like offline message recovery attacks (OMRA) and algorithm substitution attacks (ASAs). By eliminating the need for... 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 >

Securely transmitting a large amount of data in a short time is a serious challenge in today's digital age. Cryptographic primitives can significantly alleviate this problem. The invention of digital signatures represents a major advance in this area. The Certificateless Aggregate Signature (CLAS) scheme is a cryptographic primitive that greatly reduces the computation cost by aggregating several signatures into a single short signature. However, the costs of aggregate signatures have not been reduced to the desired extent. In this paper, we propose a certificateless aggregate signature scheme that requires only two bilinear pairing operations to verify both a single signature and an aggrega... More >

Federated learning (FL) enables decentralized model training and enhances user privacy by keeping data on local devices. Despite these advantages, FL remains vulnerable to sophisticated adversarial attacks. Federated recommender systems (FRS), an important application of FL, are particularly susceptible to threats such as model poisoning. In this paper, we propose DyMUSA, a novel model poisoning attack tailored for FRS. DyMUSA exploits systemic vulnerabilities through dynamic user selection and adaptive poisoning strategies. Specifically, it leverages the Isolation Forest algorithm to identify anomalous users and generate poisoned gradients that compromise the integrity of the recommender sy... More >

The rapid proliferation of smart home IoT devices has introduced unprecedented cybersecurity vulnerabilities, necessitating scalable and privacy-preserving intrusion detection systems (IDS). Federated Learning (FL) offers a promising decentralized approach by training models locally without sharing raw data, but it remains susceptible to poisoning attacks and relies on a vulnerable central aggregator. This paper presents a novel blockchain-enhanced FL framework tailored for smart home IDS, integrating multiple consensus mechanisms—Proof-of-Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Proof-of-Authority (PoA)—for the first time in this context. Our approach uniquely combin... More >