Federated Learning (FL) has gained significant attention for enabling collaborative model training while preserving data privacy. However, protecting the intellectual property (IP) of models in FL, particularly against model theft by malicious clients, remains a critical challenge. Existing works often employ watermarking techniques to embed watermarks into models for ownership verification, but most of them can only verify ownership after the model has been stolen and cannot proactively defend against malicious clients attempting to steal the model. To address this limitation, this paper proposes FedLock, a novel server-side watermarking mechanism designed to resist model theft and safeguard the IP of global models. Specifically, FedLock leverages Split Federated Learning (SFL) to partition the model, effectively preventing malicious clients from accessing the complete set of global model parameters. To enhance security, FedLock incorporates an autoencoder for label encoding, safeguarding the server-side model from reconstruction attacks. Furthermore, FedLock introduces an additional backdoor client to embed a black-box watermark into the global model, enabling remote verification of model ownership. Experimental results demonstrate that FedLock achieves robust watermarking with minimal impact on model performance, effectively resisting various attacks, including model theft, pruning, and fine-tuning.

As edge devices grow smarter and application scenarios become more diverse, users' demands for lower latency and higher efficiency in data storage and processing have risen sharply. Individual edge devices and nodes are no longer sufficient to meet these expanding storage requirements. Consequently, developing efficient, low-latency, and cost-effective solutions for collaborative storage across edge devices and nodes has become a critical challenge. In this paper, we present a framework for the transaction and pricing of storage resources in an edge computing environment involving multiple edge service providers, to address trust and incentive issues in storage resource collaboration. Firstly, we propose a secure and decentralized storage resource trading mechanism by leveraging blockchain technology and smart contracts. We introduce Proof of Transaction Expectation (PoTE), an efficient, reliable, and lightweight consensus mechanism, to ensure transaction transparency, openness, and non-repudiation. Secondly, we introduce a game theory-based storage resource pricing model, where a leader interacts with multiple followers to optimize profits while maintaining service quality. To address dynamic pricing and storage resource allocation problems under incomplete information, we propose the Stackelberg Game Approach based on Multi-Agent Reinforcement Learning (SGA-MARL), which formulates the optimal pricing and trading share decisions in the two-stage Stackelberg game as a stochastic Markov Decision Process (MDP). Simulations and prototype testing validate the effectiveness of the proposed system, with results showing that the PoTE consensus achieves up to 40% higher throughput than Proof-of-Work while reducing latency by over 50% compared to PBFT, and the SGA-MARL algorithm improves leader profit by approximately 30% and resource satisfaction rates by over 80% compared to baseline methods like MA-PPO and DQN.

Mobile edge computing is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers. However, within small-cell networks, the user mobilities can result in uneven spatio-temporal loads, which have not been well studied by considering adaptive load balancing, thus limiting the system performance. Motivated by the data analytics and observations on a real-world user association dataset in a large-scale WiFi system, in this paper, we investigate the mobility-aware online task offloading problem with adaptive load balancing to minimize the total computation costs. However, the problem is intractable directly without prior knowledge of future user mobility behaviors and spatio-temporal computation loads of edge servers. To tackle this challenge, we transform and decompose the original task offloading optimization problem into two sub-problems, i.e., task offloading control (ToC) and server grouping (SeG). Then, we devise an online control scheme, namedMOTO(i.e.,Mobility-awareOnlineTaskOffloading), which consists of two components, i.e., Long Short Term Memory based algorithm and Dueling Double DQN based algorithm, to efficiently solve theToCandSeGsub-problems, respectively. Extensive trace-driven experiments are carried out and the results demonstrate the effectiveness ofMOTOin reducing computational costs of mobile devices and achieving load balancing when compared to the state-of-the-art benchmarks.

Systems that integrate distributed databases and existing blockchain platforms have recently emerged, which conveniently leverage their respective strengths to build efficient, secure, and usable data sharing and collaboration environments for different organizations. However, the performance of such systems can be limited by the native blockchain platforms due to the high latency of transactions. In this paper, we present CoralDB, a bottom-up fully redesigned hybrid system of blockchain and database, aimed at enabling untrusted organizations to collaborate and share data efficiently and securely at the database level. The storage layer of CoralDB ensures data security and system throughput through key modules such as customized block structure, consensus mechanism, and transaction pool. On top of the storage layer, a database layer is introduced, which extends the blockchain of the storage layer by incorporating connection pools, collaborative tables, and query interfaces, to enhance the usability and efficiency of data collaboration and sharing. Extensive experimental results demonstrate that CoralDB provides security assurances at the level of blockchain and enables efficient decentralized data collaboration and sharing.

Mobile-edge computing (MEC) is a promising approach to support high-quality time-sensitive applications. With the increasing number of mobile devices, achieving efficient service migration management has become nontrivial in MEC. In addition, the service migration issue is difficult to be solved in real time due to user mobility and dynamic network conditions. In this article, we investigate the mobility-aware service migration problem in MEC by introducing a data-driven framework. First, service migration is formulated as an optimization problem for minimizing the long-term system delay that consists of computing, communication, and migration delays. Second, we propose a Mobility-aware Service Migration scheme, named MSM, consisting of three layers: 1) the data collection layer; 2) the association patterns analysis layer; and 3) the service migration layer. Specifically, we first collect users’ historical Wi-Fi traces to mine the association patterns. We then design a user management mechanism to reduce the complexity of decision making by using user association patterns. Finally, we formulate the service migration as a 2-D-Markov decision process and devise a deep reinforcement learning (DRL)-based algorithm to obtain service migration decisions in a large-scale MEC scenario. Extensive data-driven experiments are conducted to demonstrate the efficacy of MSM in reducing the system delay.

In this article, we proposeFLAGfor flexible, accurate, and long-time user load prediction in a large-scale WiFi system.FLAGenables prediction customization in both time granularity and prediction length. Under an operating WiFi system with more than 7000 APs, a reference implementation ofFLAGis developed, which consists of three major components. Fordata acquisition, we process 25 074 733 association records contributed by 55 809 users, to extract the ground truth of AP-level user load. Forfeature extraction, we perform a comprehensive data analytics to mine vital features to label each AP, which are extracted and classified into three categories, i.e., individual features, spatial features, and temporal features. For themodel design, we design a deep recurrent neural network (RNN) model, which contains two separate RNNs, i.e., the encoder RNN and decoder RNN. Particularly, the sequential feature vectors are injected into the encoder RNN to learn the “semantic” information, based on which the decoder RNN conducts sequential AP-level predictions. As the semantic vector is injected for each time step prediction, it can effectively reduce the accumulated prediction errors, which enable long period of time predictions. Real data set-based experiments corroborate the efficacy ofFLAG.