Ethernet-SSDs incorporate built-in Ethernet connectivity, allowing them to function as standalone network-attached storage devices. This architecture enables efficient and scalable disaggregated storage by providing direct network access without host dependencies. Understanding their internal architecture and fine-grained performance interactions is critical for advancing this emerging design, yet is difficult to study on proprietary hardware. In this paper, we present EEMU, a configurable and accurate Ethernet-SSD emulator built on the FEMU framework. EEMU reproduces the end-to-end NVMeoF I/O translation path and models latency across three key components: the Network Interface, the NVMeoF Target Module, and the AXI Bus. It further employs a fine-grained parametric latency model that decomposes end-to-end latency into component-level contributions, enabling systematic exploration of architectural trade-offs via controlled parameterization. Experiments show that EEMU closely matches both component-level behaviors and end-to-end performance across diverse Ethernet-SSD configurations.

The rise of collaborative AI, particularly in distributed Mixture-of-Experts (MoE) systems, has created a critical challenge: how to ensure trust and transparency when aggregating proprietary models from different providers. To address this, we introduce a novel cryptographic protocol ZQ-WMA that enables verifiable and privacy-preserving online learning. Our method integrates zero-knowledge proofs with a quantized version of the Weighted Majority Algorithm, allowing a central aggregator to publicly prove it is honestly combining expert advice and updating weights according to the agreed-upon rules, all without revealing any confidential model parameters.This approach ensures that expert contributions are evaluated fairly and protects valuable intellectual property. Our analysis reveals that the quantization necessary for the zero-knowledge proofs can counter-intuitively enhance prediction accuracy, a phenomenon we attribute to the maximal entropy random walks. Furthermore, our benchmarks demonstrate the efficiency of this method, showing proof generation complexity less than 10% of a standard SHA256 hash function, with O(1) proof size and verification time. This work provides a practical and scalable framework for building trustworthy collaborative AI systems.

With the development of quantum blockchain, the quantum consensus protocols have garnered increasing attention, which play a crucial role in driving the implementation of quantum blockchains. However, existing protocols, derived from the classical consensus algorithms, face practical application challenges due to current quantum technology limitations. The first challenge is the bottleneck in generating large-scale entangled quantum states. The second challenge arises from the generation of malicious quantum states. The final challenge involves privacy concerns. To address these challenges, we propose a practical iterative QUantum consensus protocol with sharding construction, namely, Q-Union. In fact, Q-Union employs an iterative consensus algorithm where participating nodes are divided into multiple smaller shards, with the consensus process occurring within the current shard, and new shards are involved only if consensus is not achieved. Leveraging Greenberger-Horne- Zeilinge states and Aharonov states, Q-Union harnesses the advantages of quantum mechanics to achieve anonymous consensus, protecting the private information of participating nodes. Additionally, by integrating state verification, Q-Union ensures the correctness of the consensus procedure in the presence of malicious nodes generating adversarial quantum states. Finally, it is proven that Q-Union can also defend against Byzantine attacks from adversarial nodes, maintaining the same security level as traditional non-sharded consensus protocols. Specifically, it consistently outputs the correct consensus when the fraction of adversaries among participating nodes is less than 1/2 with synchronous communication. Both the theoretical analysis and performance illustration demonstrate the superior performance of the proposed Q-Union compared to state-of-the-art protocols.

Harnessing the benefits of blockchain, such as decentralization, immutability, and transparency, to bolster the credibility and security attributes of federated learning (FL) has garnered increasing attention. However, blockchain-enabled FL (BFL) still faces several challenges. The primary and most significant issue arises from its essential but slow validation procedure, which selects high-quality local models by recruiting distributed validators. The second issue stems from its incentive mechanism under the transparent nature of blockchain, increasing the risk of privacy breaches regarding workers’ cost information. The final challenge involves data eavesdropping from shared local models. To address these significant obstacles, this paper proposes a Blockchain-enabled Incentivized and Secure Federated Learning (BIT-FL) framework. BIT-FL leverages a novel loop-based sharded consensus algorithm to accelerate the validation procedure, ensuring the same security as non-sharded consensus protocols. It consistently outputs the correct local model selection when the fraction of adversaries among validators is less than$1/2$with synchronous communication. Furthermore, BIT-FL integrates a randomized incentive procedure, attracting more participants while guaranteeing the privacy of their cost information through meticulous worker selection probability design. Finally, by adding artificial Gaussian noise to local models, it ensures the privacy of trainers’ local models. With the careful design of Gaussian noise, the excess empirical risk of BIT-FL is upper-bounded by$\mathcal {O}(\frac{\ln n_{\min}}{ n_{\min}^{3/2}}+\frac{\ln n}{n})$, where$n$represents the size of the union dataset, and$n_{{\min}}$represents the size of the smallest dataset. Our extensive experiments demonstrate that BIT-FL exhibits efficiency, robustness, and high accuracy for both classification and regression tasks.

The application of machine learning (ML) in data analytics has become widespread across various fields, including healthcare, finance, marketing, and many others. Current research has extensively explored ML model markets, which are typically coordinated by a single broker acting as an intermediary between data providers and model buyers. However, this solution poses significant challenges due to the monopolistic control it grants the broker. Therefore, we propose a multi-broker market model to overcome these issues. The decision-making processes among multiple brokers are formulated as a multi-stage Stackelberg game designed to maximize the profits of each broker. Subsequently, the existence and uniqueness of nash Equilibrium for brokers’ strategies are proven. We conduct simulation experiments using four types of cost functions for two brokers. The simulation results demonstrate that brokers can reach a Nash Equilibrium through the proposed market model.

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.e., tasks for which the timing and type are known in advance) and do not protect the privacy of worker bids. In a dynamic and resource-constrained environment, tasks are often uncertain (i.e., the platform lacks a priori knowledge about the tasks) and worker bids may be vulnerable to inference attacks. This paper presents an incentive mechanism HERALD*, that takes into account the uncertainty and hidden bids of tasks without real-time constraints. Theoretical analysis reveals that HERALD* satisfies a range of critical criteria, including truthfulness, individual rationality, differential privacy, low computational complexity, and low social cost. These properties are then corroborated through a series of evaluations.

Resistive random access memory (ReRAM)-based processing-in-memory (PIM) DNN accelerators have shown great potential in improving model efficiency and saving energy. To further improve memory and computation efficiency, model weight sparsity has been widely explored in ReRAM-based accelerator designs. However, these optimized accelerators rarely touched the model activation sparsity. In this paper, we observe that there exist plenty sparse rows/columns in the DNN model activation matrix which have negligible effect on accuracy, termed as insensitive rows/columns. Pruning them has little impact on model accuracy but would have significant potential to improve the performance and energy efficiency of DNN accelerators. Therefore, we propose a new ReRAM-based PIM accelerator, named as RapPIM, to take advantage of the model activation sparsity. In RapPIM, we first propose an insensitive activation rows/columns pruning method to search and prune the insensitive rows/columns. Then, we present an activation low-bits skipping strategy and a forward propagation delay hiding strategy to further improve model performance and minimize the latency of activation pruning on forward propagation. Our evaluations with several well-known DNN models show that the RapPIM achieves up to 2.40× speedup and 44.82% power reduction compared with the state-of-the-art ReRAM-based accelerator.

Resistive Random-Access Memory (ReRAM) based Processing-in-Memory (PIM) frameworks are proposed to accelerate the working process of DNN models by eliminating the data movement between the computing and memory units. To further mitigate the space and energy consumption, DNN model weight sparsity and weight pattern repetition are exploited to optimize these ReRAM-based accelerators. However, most of these works only focus on one aspect of this software/hardware co-design framework and optimize them individually, which makes the design far from optimal. In this paper, we propose PRAP-PIM, which jointly exploits the weight sparsity and weight pattern repetition by using a weight pattern reusing aware pruning method. By relaxing the weight pattern reusing precondition, we propose a similarity-based weight pattern reusing method that can achieve a higher weight pattern reusing ratio. Experimental results show that PRAP-PIM achieves 1.64× performance improvement and 1.51× energy efficiency improvement in popular deep learning benchmarks, compared with the state-of-the-art ReRAM-based DNN accelerators.

As smart devices are becoming increasingly common in people’s daily lives, privacy and security concerns make data collection expensive and limited, which further hinder the development of data-driven tasks. This paper studies how to better conduct private data trading via a novel generator method rather than direct trading of raw data. This new method facilitates more convenient data transactions by generator, protects the privacy of data owners and is satisfactory in terms of privacy compensation and query pricing. In detail, we propose RARIEA, a market framework for tRading privAte data geneRators based on GAN under rényI diffErential privAcy, which involves data owners, a data broker, and data consumers. To start, the broker employs the GAN training generator to augment the data to relieve the data shortage, introducing noise into its training process to preserve the owners’ privacy. After that, the broker uses rényi differential privacy to quantify the privacy loss at the data item level during the GAN training process and compensates each owner according to their respective privacy policies. Finally, the data broker charges each of the data consumers for their queries, where the price is lower bounded by the total privacy compensation. We then evaluate the performance of RARIEA on classic data sets: MNIST, Fashion-MNIST, and CelebA. The analysis and simulation results reveal that the generator provided by RARIEA can not only meet the data consumers’ demand for quantity and quality but also protect the owners’ privacy. In addition, RARIEA not only allows finer control over data owner compensation, but also excels at controlling the data broker’s revenue to improve market efficiency while ensuring fairness, balance, and monotonicity of pricing.