Federated machine unlearning service is an emerging Machine-Learning-as-a-Service (MLaaS) paradigm which supports the request of removing or forgetting the influence of a specific group of data from federated learning services. While current federated unlearning methods work well in removing instances on individual clients, they encounter challenges in addressing multiple forms of unlearning requirements, such as heterogeneous learning models and diverse unlearning contents. In this paper, we propose novel federated few-shot learning and unlearning models inspired by distillation learning. Firstly, we propose a federated metric learning model where only prototypes are transmitted between the server and clients with limited samples. The training data with different input dimensions on local clients are transformed into abstract prototypes with the same length, enabling collaborative training of heterogeneous models among clients. Then, we propose an efficient federated metric unlearning method, where the temporarily stored prototypes are utilized as teacher knowledge to guide and accelerate the retraining process for each unlearning scenario. The complexity of the federated metric unlearning method is analyzed to show the computation time and communication efficiency. Experimental results demonstrate that our approach outperforms baseline methods in terms of accuracy while effectively removing various requested unlearning contents.

Multimodal learning integrates diverse modalities to enhance robustness, yet real-world scenarios suffer from heterogeneous imbalance phenomena (noise interference, modality partial missing, intermodal information disparities), degrading performance through biased feature representations. Existing methods fail to adaptively modulate models under dynamic imbalance conditions. We propose GMML, a framework dynamically balancing multimodal gradients to counteract imbalance-induced biases: i) An imbalance-aware gradient modulation adaptively identifies contributions with smooth weight transitions to balance conflicting gradients; ii) A parameter constraint method enforces ℓ2-norm constraints on encoders, suppressing parameter oscillations and blocking noisy updates under modality missing/noise. Theoretically, GMML achieves a larger certified radius upper bound for complex imbalances, with convergence radius analysis providing theoretical guarantees. Experiments demonstrate superior robustness against three imbalance types, outperforming state-of-the-art by 3.3% and 2.3% in accuracy on KS and UCF-101 benchmarks. series Code: https://github.com/zhangzikai-security-ML/GMML.

With the advancement of intelligent and networked technology, the Industrial Internet of Things (IIoT) faces an escalating threat from cyberattacks, especially by Advanced Persistent Threat (APT) attacks. These novel and complex attacks, characterized by their dynamic nature and life-long duration, pose significant challenges to existing security protection methods. The challenges are twofold, i.e., sparse reward problem in the long-lasting attack, and partial observation of attack actions. To this end, we propose a Security-as-a-Service based reinforcement learning method, namely Attention Augmented Dueling Deep Q-learning Network (AD2QN), to make real-time defense strategies for the hot standby IIoT. First, we build the attack-defend confrontation model as black boxes interact with the IIoT environment to play a long-lasting partially observable zero-sum stochastic game on the server. Then, to dynamically generate optimal defense strategies as the service, AD2QN is proposed employing information completion and prediction to more informed action selection. Furthermore, AD2QN utilizes an iteratively updated reward network to deal with the sparse reward problem. Extensive simulation results shown that the defense strategies generated by our method have a higher defense success rate and a stable defense performance with the average success rate of 0.7384, while the average success rate of baseline methods was 0.7375, in the best case.

Abstract Decentralized storage of data is one of the typical applications in the blockchain network. However, most of the existing works neglected the storage balancing problem in the blockchain network, which has an immediate impact on the availability and stability of the network. Therefore, this paper proposes a storage balancing problem for non-local data storage in the blockchain network and proves that the problem is non-deterministic polynomial (NP)-hard. The criterion of the storage balance is established by a balanced coefficient in the proposed scheme. A heuristic matching algorithm (HMA), a genetic algorithm (GA) and a tabu search algorithm (TSA) are customized to solve the problem of imbalanced storage formalized in this paper. Compared with our previous algorithm fast matching algorithm (FMA), experimental results demonstrate that HMA achieves better performance in terms of accuracy, computation overhead and storage overhead. Specifically, the computation overhead of HMA is lower than that of FMA by 84.45% on average, whereas the storage overhead of HMA is lower than that of FMA by 32.26% on average. By using the initial solution of HMA, TSA achieves the highest accuracy among GA, TSA and moth-flame optimization (MFO). Meanwhile, by using the initial solution of FMA, TSA achieves the highest accuracy among GA, TSA and MFO.

Malware detection is a vital task for cybersecurity. For malware dynamic behavior, threats come from a small number of Application Programming Interfaces (APIs) embedded in the API sequences, which are easily ignored or obfuscated in the detection process. Prior works proposed graph-based learning methods to solve this problem using API-level behavior relations. However, the malware detection is still challenging, due to the ignore of the temporal correlation between malicious behaviors. In this study, we model the software behaviors with multiscaled API graph sequences to represent API-level behaviors as well as graph-level temporal behavior correlations. We then propose a novel Dynamic Evolving Graph Convolutional Network (DEGCN) model to capture dynamic evolving pattern of both local API-level and global graph-level software behaviors. In particular, we first extract the API-level (node) representations to capture the directed graph representations for each time slot. We then propose a Graph-encoding-based Gate Recurrent Unit (GGRU) network to capture the graph-level evolving features and their evolving status. The graph features of different time slots and different graph scales are concatenated to detect whether the software is benign or malicious. Our evaluation with two public benchmarks reports that DEGCN achieves the best performance compared with state-of-the-art algorithms.

Recently, federated learning has been used by Network Intrusion Detection Systems (NIDSs) to expanding data features while preserving data privacy. However, non-independent and identically distributed (non-iid) datasets weaken the model performance of federated learning. In this paper, we present a novel Federated Intrusion Detection System(FIDS) to classify the differences of DDoS attacks from the non-iid dataset. A prototypical weight is introduced to measure the correlations between global data space and local data spaces. We then explore the feature combinations of abnormal behaviors and extract extra features from original data in preprocessing steps. Experimental results show that the FIDS improves the performance of training stability and convergence rate compared to two baselines.

Big data for social transportation brings unprecedented opportunities for us to solve the transportation problems that cannot be solved by traditional methods and build the next generation of the intelligent transportation system (ITS). As one of the important functions of the ITS, supply-demand difference prediction for autonomous vehicles provides a decision basis for its control. In this article, a new learning process is proposed with Multiple feature Extraction and Fusion utilizing the combination of deep and shallow Features (MEFF) (the spatial deep features, (short and long) temporal deep features, and fuzzy shallow (semantic) features). The spatial deep features are captured with residual network and dimension reduction in spatial deep block. The fuzzy shallow (semantic) features are captured with multiattention fuzzy mechanism in the fuzzy shallow block. With the fused spatial deep features and fuzzy shallow features, the temporal deep features are captured with long short-term memory (LSTM) and attention mechanism in the temporal and prediction block to get the final prediction results. Based on two different distributions of membership attention (mean distribution and Gaussian distribution) in the fuzzy shallow block, our process MEFF has two methods, i.e., MEFF-mean method and MEFF-Gaussian method. Extensive experiments show that our methods provide more accurate and stable prediction results than the existing state-of-art-methods.

As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current day's historical data is available or several days' historical data are available. These two models jointly consider the spatial, temporal, and semantic relations. Spatial dependency is captured with residual network and dimension reduction. Short term temporal dependency is captured with LSTM. Long term temporal dependency and temporal shifting are captured with LSTM and attention mechanism. Semantic dependency is captured with multi-attention mechanism. Extensive experiments show that our frameworks provide more accurate prediction results than the existing methods.

Abstract The task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station’s storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods.

In this paper, we develop an access controller management model which provides new opportunities for further reducing the computation repetition and data transmission redundancy for Mobile Edge Computing (MEC) in 5G network. We propose novel algorithms for solving the offloading problem with consideration of tradeoff between energy consumption and the amount of offloaded data under constraint of overall task computation time. For sequential topology applications, we develop a dynamic programming algorithm to produce optimal solutions. For general topology applications, a critical-path based heuristic algorithm is proposed by repeatedly identifying partial critical path (PCP) from the application task graph and calculating optimal solution for the PCP by performing the proposed dynamic programming algorithm. In addition, the interference of parallel data transmission between tasks (one-to-many, manyto-one and many-to-many) using single channel is taken into consideration. Experimental results demonstrate the effectiveness of our proposed method.