China's robust renewable energy sector expansion has spurred academic inquiry into the underlying driving forces. Recent studies have highlighted the correlation between the growth of renewable energy and project financing and the pivotal role of information management in financing. Based on this, we use business data from Chinese listed companies from 2007 to 2021 to investigate the relationship between renewable energy business development and financing. Furthermore, we introduce social and enterprise information to explore their impact on this relationship. Our findings reveal that: (1) Expansion of renewable energy businesses enhances fund availability mainly through endogenous financing. (2) “Anti-dumping and countervailing” (“Double-anti”) investigations significantly hinder financing for expanding renewable energy businesses, while industrial policies mitigate these effects. (3) Complex international backgrounds combined with domestic market-government interactions promote renewable energy enterprises to apply information management technology for financing.

Federated Learning (FL) allows multiple data owners to jointly train machine learning models by sharing local models instead of raw private data, alleviating data privacy concerns. However, as the local computation of data owners is unpredictable, it increases its vulnerability to Byzantine attacks, where compromised data owners submit abnormal local models that can severely degrade global model accuracy. Existing Byzantine-robust FL methods depend on a semi-honest server executing predefined Byzantine-robust aggregation rules (ByRules) to filter out abnormal local models, but these methods fail when the server is compromised. Although recent serverless Byzantine-robust FL approaches mitigate the risk of a compromised server, they suffer from challenges in achieving consensus on ByRules and impose a heavy burden on privacy protection. In this paper, we propose ROBY, a novel serverless FL framework that extends existing ByRules to a decentralized setting, effectively defending against Byzantine attacks and ensuring privacy protection for local models. ROBY introduces a shared, dynamically updated consensus dataset that serves as a reliable benchmark for applying ByRules and enabling efficient consensus on ByRules among decentralized data owners. Moreover, we design a dual-layer privacy shielding strategy in ROBY to protect local model privacy without sacrificing global model accuracy or incurring extra computational and communication overhead. Extensive evaluations demonstrate that ROBY substantially enhances both Byzantine robustness and privacy protection compared to server-based FL methods.

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets.