Sharding protocols in cryptocurrencies are dedicated to improving system throughput and reducing cross-shard transaction latency. However, they face the challenge of state explosion due to their stateful design. An intuitive solution is state sharding. Existing state sharding protocols aim to reduce the incidence of cross-shard transactions and balance transaction load. Nevertheless, it inevitably introduces the complex and sequential processing of cross-shard transactions and neglects the security of state data. To address these challenges, we propose SecChain, a secure stateless sharded blockchain via a novel state commitment in this paper. SecChain fully exploits the historical characteristics of transactions and designs the parallel cross-shard transaction execution based on asynchronous prepaid accounts. We then introduce a novel state commitment scheme to ensure the security of state data and improve the efficiency of transaction validation. In addition, we optimize the on-chain and off-chain storage by integrating an incremental partial state trie on-chain and a full trie with proofs off-chain. We perform a comprehensive evaluation using real-world workloads and results demonstrate that SecChain's throughput is 1.75× that of BrokerChain, and its latency is reduced by 7×. Furthermore, SecChain significantly reduces both proof overhead and storage overhead.

Federated Learning (FL) enables collaborative model training while preserving data privacy, but it is highly vulnerable to backdoor attacks. Most existing defense methods in FL have limited effectiveness due to their neglect of the model's over-reliance on backdoor triggers, particularly as the proportion of malicious clients increases. In this paper, we propose FedBAP, a novel defense framework for mitigating backdoor attacks in FL by reducing the model's reliance on backdoor triggers. Specifically, first, we propose a perturbed trigger generation mechanism that creates perturbation triggers precisely matching backdoor triggers in location and size, ensuring strong influence on model outputs. Second, we utilize these perturbation triggers to generate benign adversarial perturbations that disrupt the model's dependence on backdoor triggers while forcing it to learn more robust decision boundaries. Finally, we design an adaptive scaling mechanism to dynamically adjust perturbation intensity, effectively balancing defense strength and model performance. The experimental results demonstrate that FedBAP reduces the attack success rates by 0.22%-5.34%, 0.48%-6.34%, and 97.22%-97.6% under three types of backdoor attacks, respectively. In particular, FedBAP demonstrates outstanding performance against novel backdoor attacks.

Federated learning (FL), as an emerging distributed machine learning paradigm, aims to achieve collaborative training of a global model without sharing clients’ local data, thereby addressing user privacy and data silo issues. However, due to the distributed architecture of FL and the invisibility of local training, it is highly susceptible to poisoning attacks. While numerous poisoning defense methods have been proposed, they often suffer from limited defense effectiveness and overly strong security assumptions. To address this problem, we propose FedTPD, a dual gradient evaluation-based defense method against poisoning attacks in FL. Specifically, we first design an adaptive representative gradient selection mechanism, which involves adaptively clustering local gradients and selecting representative gradients from different clusters. Secondly, we propose a client-assisted model evaluation mechanism, wherein a reliable client is selected as an evaluator to assess the representative gradients from various clusters. Finally, we develop a bias-corrected model aggregation mechanism that aggregates the local gradients of each cluster based on the trust scores of their representative gradients, thereby reducing the impact of poisoning attacks on the global model. Experimental results on three typical datasets demonstrate that the proposed FedTPD can effectively resist various poisoning attacks. In particular, compared with state-of-the-art methods, the global model’s maximum and average accuracies on the CIFAR-10 and CIFAR-100 datasets are improved by 2.79%–28.15% and 0.79%–31.34%, and 3.48%–22.18% and 2.56%–22.06%, respectively.