Hamed Aboutorab

Milad Baghalzadeh Shishehgarkhaneh, Robert C. Moehler, Yihai Fang et al.

The construction industry in Australia is characterized by its intricate supply chains and vulnerability to myriad risks. As such, effective supply chain risk management (SCRM) becomes imperative. This paper employs different transformer models, and train for Named Entity Recognition (NER) in the context of Australian construction SCRM. Utilizing NER, transformer models identify and classify specific risk-associated entities in news articles, offering a detailed insight into supply chain vulnerabilities. By analysing news articles through different transformer models, we can extract relevant entities and insights related to specific risk taxonomies local (milieu) to the Australian construction landscape. This research emphasises the potential of NLP-driven solutions, like transformer models, in revolutionising SCRM for construction in geo-media specific contexts.

Nazatul Haque Sultan, Yan Bo, Yansong Gao et al.

Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. However, local model or weight updates can still leak sensitive information. Secure aggregation protocols mitigate this risk by ensuring that only the aggregated updates are revealed. Among these, single-setup protocols, where key generation and exchange occur only once, are the most efficient due to reduced communication and computation overhead. However, existing single-setup protocols often lack support for dynamic user participation and do not provide strong privacy guarantees such as forward and backward secrecy. \par In this paper, we present a novel secure aggregation protocol that requires only a single setup for the entire FL training. Our protocol supports dynamic user participation, tolerates dropouts, and achieves both forward and backward secrecy. It leverages lightweight symmetric homomorphic encryption with a key negation technique to mask updates efficiently, eliminating the need for user-to-user communication. To defend against model inconsistency attacks, we introduce a low-overhead verification mechanism using message authentication codes (MACs). We provide formal security proofs under both semi-honest and malicious adversarial models and implement a full prototype. Experimental results show that our protocol reduces user-side computation by up to $99\%$ compared to state-of-the-art protocols like e-SeaFL (ACSAC'24), while maintaining competitive model accuracy. These features make our protocol highly practical for real-world FL deployments, especially on resource-constrained devices.