Federated Learning (FL) enhances data privacy for End Equipment Workers (EWs) by enabling the sharing of model parameters instead of raw data. However, energy constraints and individual self-interest may discourage EWs from participating or slow down training, ultimately affecting the performance of the global FL model. To address these challenges, we propose a three-stage Stackelberg game-based framework that leverages wireless power to incentivize participation while ensuring the successful completion of FL tasks. In this framework, the Base Station (BS) publishes FL task and seeks to obtain an improved global model at a reduced cost. EWs train local models, aiming to maximize their payments while minimizing energy consumption. Meanwhile, the Charging Service Provider (CSP) supplies energy to EWs via Wireless Power Transfer (WPT) during model training and uploading, charging appropriate fees for the service. We employ the backward induction method to analyze the proposed game problem, proving the existence of a unique Stackelberg equilibrium and Nash equilibrium. Furthermore, we propose the Trust Region Method (TRM) to solve the unit payment strategy problem of BS. Extensive simulations validate that our method consistently outperforms benchmark schemes, achieving higher average utility across a wide range of scenarios.
Blockchain oracles are essential for enabling smart contracts to access and interact with off-chain data, where its security and trustworthiness are crucial for preserving the overall reliability and integrity of blockchain-based systems. However, existing reputation-based blockchain oracles are vulnerable to Sybil and collusion attacks. To address these issues, trusted execution environments (TEEs) have been leveraged to construct blockchain oracles. While these TEE-enabled oracles provide resistance against the above attacks, they typically depend on a centralized Root of Trust (RoT), which introduces risks such as root key pre-provision risks and single points of failure. Moreover, they often struggle to maintain trust over time, making them unsuitable for application scenarios that require long-term guaranteed trust. In this paper, we propose EnduraTrust, a highly secure blockchain oracle network empowered by a decentralized Root of Trust (DRoT), which can overcome single points of failure, offer trust sustainability, minimize the security risk of root key pre-provision, and provide essential participation incentive. Concretely, it adopts Physically Unclonable Functions (PUFs) to generate device-specific root keys, eliminating the risks associated with centralized key pre-provision. To confront mobile attacks and ensure sustained trust, it employs an off-chain process to generate attestation reports, which are then verified onchain through a non-interactive protocol. This protocol ensures robust integrity verification for Individual Root of Trust (IRoT) nodes while minimizing latency and overhead. In addition, we design a fair and secure IRoT node selection algorithm to form a trustworthy oracle committee to offer oracle services, protecting against Sybil and collusion attacks to which existing oracles are prone. Particularly, we apply an incentive mechanism to encourage oracle node participation and reliable behavior. We validate EnduraTrust through formal security analysis and performance evaluation based on prototype implementation. Our results show that EnduraTrust enhances root key security, realizes decentralization, offers fair oracle node selection, ensures sustained trust, exhibits strong robustness, and provides participation incentive. It surpasses existing decentralized attestation schemes in multiple dimensions, including security, contract execution efficiency, attestation protocol performance, and scalability. Besides, its performance regarding oracle response aggregation and aggregated response verification confirming its effectiveness.
We study a setting where an intelligent model (e.g., a pre-trained neural network) infers the real-time value of a target signal using data samples transmitted from a remote source. The transmission scheduler decides (i) the freshness of packets, (ii) their length (i.e., the number of samples they contain), and (iii) when they should be transmitted. The freshness is quantified using the Age of Information (AoI), and the inference quality for a given packet length is a general function of AoI. Previous works assumed i.i.d. transmission delays with immediate feedback or were restricted to the case where inference performance degrades as the input data ages. Our formulation, in addition to capturing non-monotone age dependence, also covers Markovian delay on both forward and feedback links. We model this as an infinite-horizon average-cost Semi-Markov Decision Process. We obtain a closed-form solution that decides on (i) and (iii) for any constant packet length. The solution for when to transmit is an index-based threshold policy, where the index function is expressed in terms of the delay state and AoI at the receiver. In contrast, the freshness of the selected packet is a function of only the delay state. We then separately optimize the value of the constant packet length. Moreover, we also develop an indexbased threshold policy for the time-variable packet length case, which allows a complexity reduction. In simulation results, we observe that our goal-oriented scheduler drops inference error down to one-sixth with respect to the age-based scheduling of unit-length packets.
In inter-datacenter (DC) elastic optical networks (EONs), dynamic service function chain (SFC) requests arrive and expire on-the-fly, posing significant challenges for efficient resource allocation. Existing heuristic and deep reinforcement learning (DRL)-based methods provide certain optimizations but rely on a full-connection-oriented provisioning scheme, which reserves the entire resource chain for the lifecycle of each SFC request, resulting in suboptimal resource utilization. To overcome these limitations, we propose a dynamic provisioning with store-and-forward scheme to address the dynamic SFC mapping and time scheduling problems based on DRL in inter-DC EONs. In this scheme, the virtual network functions (VNFs) and the light-path provisioning acquire resources only within their respective service times, which are realized by accurate time scheduling. To facilitate the decision-making process of the DRL agent, we decompose the intricate SFC request into several SFC sub-requests, solving them one by one in turn. Evaluations with two topologies demonstrate that our proposed DRL-based method can achieve blocking probability reductions of more than 10.1% and 13.8% when compared with the baselines.
For those seeking end-to-end private communication free from pervasive metadata tracking and censorship, the Tor network has been the de-facto choice in practice, despite its susceptibility to traffic analysis attacks. Recently, numerous metadata-private messaging proposals have emerged with the aim to surpass Tor in the messaging context by obscuring the relationships between any two messaging buddies, even against global and active attackers. However, most of these systems face an undesirable usability constraint: they require a metadata-private “dialing” phase to establish mutual agreement and timing or round coordination before initiating any regular chats among users. This phase is not only resource-intensive but also inflexible, limiting users’ ability to manage multiple concurrent conversations seamlessly. For stringent privacy requirement, the often-enforced traffic uniformity further exacerbated the limitations of this roadblock. In this paper, we introduce PingPong, a new end-to-end system for metadata-private messaging designed to overcome these limitations. Under the same traffic uniformity requirement, PingPong replaces the rigid “dial-before-converse” paradigm with a more flexible “notify-before-retrieval” workflow. This workflow incorporates a metadata-private notification subsystem, PING, and a metadata-private message store, PONG. Both PING and PONG leverage hardware-assisted secure enclaves for performance and operates through a series of customized oblivious algorithms, while meeting the uniformity requirements for metadata protection. By allowing users to switch between conversations on demand, PingPong achieves a level of usability akin to modern instant messaging systems, while also offering improved performance and bandwidth utilization for goodput We have built a prototype of PingPong with 32 8-core servers equipped with enclaves and conducted a case study on a real-world messaging metadata dataset to validate our claims.
