Remote sensing change detection (RSCD) has become an essential tool in observing and analyzing geographical information. However, existing deep learning approaches dependent solely on visual modalities may encounter challenges in discerning subtle variations amidst noise interference. To overcome these issues, we propose a multiscale semantic-guided synergistic interaction network (MSSI-Net), which utilizes the advanced multimodal semantic representations for enhancing the capacity to perceive hierarchical changes. Specifically, we first devise a multiscale interaction module (MIM) which leverages multiscale attention mechanism to guide the interaction between the coarse and fine stages of different visual features. The fine-grained visual features subsequently complement the semantic features through scale weight reassignment to enhance the discriminative capability of vision-language features. Furthermore, driven by the semantic-guided synergistic interaction mechanism, our developed cross-modal feature fusion module (CFFM) exploits both homogeneous and heterogeneous features among modalities. This ensures that the generated vision-language features are semantically representative. Finally, we formulate a manifold differential perception head (MDPH) to optimize the detection of changes by efficiently fusing diverse differential feature representations, achieving comprehensive performance enhancement. Extensive experiments conducted on four benchmark datasets (LEVIR-CD, CDD, SYSU-CD and WHU-CD) indicate that the designed MSSI-Net achieves state-of-the-art performance compared to existing methods.

Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model. Although various methods exist for detecting such clients in FL, identifying malicious clients requires sufficient model updates, and hence by the time malicious clients are detected, FL models have already been poisoned. Thus, a method is needed to recover an accurate global model after malicious clients are identified. Current recovery methods rely on (i) all historical information from participating FL clients and (ii) the initial model unaffected by the malicious clients, both leading to a high demand for storage and computational resources. In this paper, we show that highly effective recovery can still be achieved based on 1) selective historical information rather than all historical information and 2) a historical model that has not been significantly affected by malicious clients rather than the initial model. In this scenario, we can accelerate the recovery speed and decrease memory consumption while maintaining comparable recovery performance. Following this concept, we introduce Crab (Certified Recovery from Poisoning Attacks and Breaches), an efficient and certified recovery method, which relies on selective information storage and adaptive model rollback. Theoretically, we demonstrate that the difference between the global model recovered by Crab and the one recovered by train-from-scratch can be bounded under certain assumptions. Our experiments, performed across four datasets with multiple machine learning models and aggregation methods, involving both untargeted and targeted poisoning attacks, demonstrate that Crab is not only accurate and efficient but also consistently outperforms previous approaches in recovery speed and memory consumption.

In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client’s influence from the global model in Federated Learning (FL) systems, thereby ensuring the “right to be forgotten” (RTBF). State-of-the-art methods for unlearning use historical data from FL clients, such as gradients or locally trained models. However, studies have revealed significant information leakage in this setting, with the possibility of reconstructing a user’s local data from their uploaded information. Addressing this, we propose Starfish, a privacy-preserving federated unlearning scheme using Two-Party Computation (2PC) techniques and shared historical client data between two non-colluding servers. Starfish builds upon existing FU methods to ensure privacy in unlearning processes. To enhance the efficiency of privacy-preserving FU evaluations, we suggest 2PC-friendly alternatives for certain FU algorithm operations. We also implement strategies to reduce costs associated with 2PC operations and lessen cumulative approximation errors. Moreover, we establish a theoretical bound for the difference between the unlearned global model via Starfish and a global model retrained from scratch for certified client removal. Our theoretical and experimental analyses demonstrate that Starfish achieves effective unlearning with reasonable efficiency, maintaining privacy and security in FL systems.

With the rapid development of low-cost consumer electronics and cloud computing, Internet-of- Things (IoT) devices are widely adopted for supporting next-generation distributed systems such as smart cities and industrial control systems. IoT devices are often susceptible to cyber attacks due to their open deployment environment and limited computing capabilities for stringent security controls. Hence, Intrusion Detection Systems (IDS) have emerged as one of the effective ways of securing IoT networks by monitoring and detecting abnormal activities. However, existing IDS approaches rely on centralized servers to generate behaviour profiles and detect anomalies, causing high response time and large operational costs due to communication overhead. Besides, sharing of behaviour data in an open and distributed IoT network environment may violate on-device privacy requirements. Additionally, various IoT devices tend to capture heterogeneous data, which complicates the training of behaviour models. In this paper, we introduce Federated Learning (FL) to collaboratively train a decentralized shared model of IDS, without exposing training data to others. Furthermore, we propose an effective method called Federated Learning Ensemble Knowledge Distillation (FLEKD) to mitigate the heterogeneity problems across various clients. FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results on the public dataset CICIDS2019 demonstrate that the proposed approach outperforms local training and traditional FL in terms of both speed and performance and significantly improves the system's ability to detect unknown attacks. Finally, we evaluate our proposed framework's performance in three potential real-world scenarios and show FLEKD has a clear advantage in experimental results.

RRAM crossbar consisting of memristor devices can naturally carry out the matrix-vector multiplication; it thereby has gained a great momentum as a highly energy-efficient accelerator for neuromorphic computing. The resistance variations and stuck-at faults in the memristor devices, however, dramatically degrade not only the chip yield, but also the classification accuracy of the neural-networks running on the RRAM crossbar. Existing hardware-based solutions cause enormous overhead and power consumption, while software-based solutions are less efficient in tolerating stuck-at faults and large variations. In this paper, we propose an accelerator-friendly neural-network training method, by leveraging the inherent self-healing capability of the neural-network, to prevent the large-weight synapses from being mapped to the abnormal memristors based on the fault/variation distribution in the RRAM crossbar. Experimental results show the proposed method can pull the classification accuracy (10%-45% loss in previous works) up close to ideal level with ≤ 1% loss.