2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 11262-11276· 0 citations· 36 references
Computer Science
Abstract
Artificial Intelligence-Generated Content (AIGC) has developed rapidly, with Diffusion Models (DMs) gaining wide attention for their superior image generation capabilities. However, the high computational cost of inference limits their deployment in resource-constrained environments such as edge computing. Cloud-edge collaboration is considered a feasible solution to improve inference efficiency, but existing studies have not fully addressed the issue of trust propagation among multiple entities. To address these, we propose a blockchain-aided trusted inference framework for cloud–edge collaborative DMs. Smart contracts are designed to automate image generation task management and ensure traceability throughout the inference process. Leveraging the step-by-step denoising nature of DMs, we introduce a Siamese Network-based Semantic Matching (SNSM) model to identify whether a new task can reuse intermediate results from historical inferences, thereby reducing redundant computation and improving efficiency. We formulate an objective function to minimize total inference latency by jointly considering queuing, transmission, and computation delays, with image quality metrics as constraints. To solve these, we propose Diffusion-Attention integrated Multi-Agent Reinforcement Learning (DAMARL), which dynamically optimizes task partitioning and scheduling between cloud and edge to reduce latency while preserving generation quality. Extensive experiments show that SNSM achieves 85.3% accuracy in reuse decisions, and DAMARL improves average reward by 17.5% $\sim ~35$ % over existing methods, demonstrating the effectiveness of our approach in enhancing DM inference efficiency and performance.
The rapid advancement of generative artificial intelligence (GAI) has led to the widespread adoption of AI-generated content (AIGC), which generally leverages generative diffusion models (GDMs) to create high-quality multimodal content. By integrating mobile edge computing (MEC) and model compression techniques, edge AIGC can provide low-latency services for mobile users (MUs) at the network edge, thereby unlocking greater potential. However, such a paradigm is vulnerable to critical security risks, such as tampering and plagiarism attacks on AIGC products. To protect the copyright of AIGC products, blockchain technology has been introduced as a promising solution. Nevertheless, most existing studies on the integrated blockchain-edge AIGC framework focus primarily on management mechanisms, while neglecting the inherent resource allocation problem for the tightly coupled AIGC inference and blockchain consensus processes, which severely limits the overall system efficiency. In this work, we propose a blockchain-enhanced edge AIGC service framework over MEC networks, where multiple edge servers (ESs) act as both model hosts generating contents for MUs and miners participating in blockchain consensus. In such a framework, we study the joint optimization of AIGC inference and blockchain consensus, including MU association, inference steps, as well as communication and computation resource allocation, aiming to maximize the total system utility. To tackle the challenges posed by mixed discrete and continuous optimization variables, we decompose the orginal problem into four sequential subproblems and propose a Joint MU association, Inference step allocation, Bandwidth allocation, Power allocation, and Streaming multiprocessor allocation (JMIBPS) algorithm based on Markov approximation, interior point method, and successive convex approximation. Simulation results validate the effectiveness of the proposed algorithm, which improves the total system utility by at least 10.21% compared with representative benchmark schemes.
Licheng Ye, Zehui Xiong, Yuan Luo et al.· IEEE Transactions on Network...· 0 citations
Blockchain technology enables decentralized trust, yet traditional blockchain networks face critical scalability limitations under large-scale deployments. Sharding improves throughput through parallel processing, but existing sharded BFT architectures still suffer from severe hierarchical coupling between shards and the verification committee. Moreover, the complex asynchronous competition and backoff/retransmission dynamics in sharded blockchain services remain largely unmodeled, leaving the network-level steady-state behavior of sharded blockchains poorly understood. To address these challenges, we propose D2S-BFT, a novel Decoupled Double-Star Byzantine Fault-Tolerant architecture, which physically decouples local intra-shard consensus from global verification. For rigorous performance evaluation, we establish a randomized-service double-star service system and cast the cross-shard competition mechanism as a finite-source Markov chain. We derive the state transition probability matrix under general load conditions, compute the extended sojourn time, and construct an end-to-end transaction on-chain latency equation that explicitly incorporates encryption overhead, network delay, and queuing delay. The resulting D2S queuing model, expressed in the non-classical Kendall notation L/G/n=2/inf/L-RSS, provides strict theoretical boundary constraints on system performance. It demonstrates that D2S-BFT can effectively alleviate transaction congestion and ensure robust operation, while also laying a rigorous analytical foundation for model-driven configuration optimization in large-scale dynamic blockchain environments.
Ji-Qiang Liu, Lijun Sun, Xiao Chen et al.· 2026 International Conferenc...· 0 citations
BEAT (Blockchain–Edge AI Trust) is presented, which closes that gap through co-design of three coupled layers: a Graph Attention Network–Long Short-Term Memory (GAT-LSTM) edge module that converts device interaction graphs into normalised trust evidence vectors.
Pavansai Ramarao Maddali, Vijay Kumar Damera, Ratna Kumar Prathipati· International Journal of Adv...· 0 citations
Fog–edge Internet of Things (IoT) systems support low-latency distributed services but remain vulnerable to coordinated attacks that evade detectors designed for independent events. Existing solutions also tend to separate attack detection, provenance verification, and accountability enforcement, leaving no unified path from relational evidence to auditable response. This study aims to develop an integrated framework that detects coordinated malicious behavior, ranks provenance relevance, verifies evidence, and activates rule-based accountability in resource-constrained edge environments. The proposed Blockchain-Fog Computing Collaborative Framework with Deep Attention-based Collusion Detection and Automated Accountability (BF3-ACDA) framework combines a hierarchical blockchain–fog architecture with an attention-based collusion graph neural network (AttnCol-GNN), whose reputation-aware attention coefficient incorporates behavioral correlation and blockchain-derived trust information. A shared attention representation supports both collusion classification and provenance ranking, while Fog-BFT consensus, Merkle verification, and smart contracts provide tamper-evident recording and severity-based enforcement. Across 30 matched independent runs on the collusion-augmented CICIoT2023 benchmark, BF3-ACDA achieved 96.80 ± 0.23% accuracy, 96.75 ± 0.21% F1-score, and 0.975 ± 0.005 AUC-ROC. Direct-transfer accuracy without target-domain fine-tuning was 94.20 ± 0.34% on NSL-KDD and 92.50 ± 0.25% on CICIDS2017. Removing detector-side reputation and verification features reduced accuracy by 3.40 percentage points, whereas replacing learned attention with mean aggregation reduced it by 1.80 points. The INT8 edge model required 12.4 MB and 58.5 ± 3.9 ms per inference. The results demonstrate a method-level coupling of coordinated-pattern detection, provenance relevance, and auditable enforcement, while supporting prototype deployment on evaluated edge, fog, and cloud platforms. As the collusion metadata were constructed for this study, the findings characterize robustness under controlled conditions rather than field performance on naturally occurring collusion.
Zehao Wang, Pei-Kang Lin, Shirong Zou et al.· Discover Computing· 0 citations
In blockchain systems, peer-to-peer (P2P) overlay networks play a crucial role in providing reliable, scalable and efficient message-delivery services to upper layers. However, the consensus layer and the underlying P2P network remain mutually opaque in existing blockchains, waiving the opportunity for further improvement. In contrast to other P2P applications, blockchain can naturally be abstracted as a state machine. We therefore leverage this abstraction to record network topologies and latencies in a trusted and coordinated manner. With this support, we propose three improvements to rotating-leader consensus protocols and their underlying P2P networks: (1) accelerating leader rotation; (2) introducing a reliable-broadcast paradigm that employs tree-based dissemination in the normal case and falls back to gossip only when necessary; and (3) constructing latency-aware dissemination trees. We integrate the above ideas into Tendermint and libp2p, and conduct empirical evaluation on Amazon EC2 platform using up to 300 nodes distributed across 10 regions. The results demonstrate that, compared with gossip-based dissemination over the same topology, our prototype improves peak throughput by up to $7.26\times$.
Yifeng Ye, Rongji Huang, Gerui Wang et al.· arXiv.org· 0 citations
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