Large language models (LLMs) provide a promising direction for learning molecular representations from text-like inputs, yet most molecular contrastive learning methods still rely on graph- or SMILES-level augmentations that may unintentionally distort chemical structure. We propose MolPACL, a prompt-augmentation-based supervised contrastive learning framework that incorporates high-level chemical semantics while preserving molecular identity. MolPACL generates multiple semantically consistent prompt views for each molecule from its SMILES string and physicochemical descriptors using diverse templates and lightweight lexical perturbations. These views are combined with task-aware class positives and negatives to form contrastive batches, and the model is trained using a supervised objective based on the Soft Nearest Neighbor loss. Experiments on MoleculeNet benchmarks show that the proposed approach achieves strong performance on both classification and regression tasks while reducing training cost, requiring no additional molecular pretraining and using a relatively small pretrained LLM.
Ali Forooghi, Luis Rueda, A. Ngom· IEEE transactions on computa...· 0 citations
Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains, where poisoning attacks can corrupt training data, manipulate model updates, or implant covert backdoors. This survey examines poisoning attacks in federated learning (FL), using centralized learning as a baseline to explain how distributed data, client heterogeneity, privacy-preserving aggregation, and untrusted coordination expand the threat surface. It positions prior surveys and synthesizes representative primary studies through an accountability-oriented lens focused on attribution, audit evidence, traceability, and forensic readiness. The review compares major attack classes, including data poisoning, model poisoning, backdoor insertion, server-side manipulation, Sybil behavior, collusion, and multi-round poisoning. It also evaluates countermeasures such as Byzantine-robust aggregation, anomaly detection, validation-based filtering, malicious-secure aggregation, authenticated update handling, provenance mechanisms, ledger-based evidence, and verifiable aggregation protocols. The analysis shows that robustness alone is insufficient for trustworthy FL unless defenses also preserve evidence that supports independent verification, post-incident reconstruction, and governance review. Persistent gaps remain in causal forensic attribution, privacy-preserving evidence governance, malicious-server threat modeling, scalable verifiability tooling, recovery after poisoning, and deployment-ready benchmarks. The survey concludes that accountable FL should be designed as an evidence-producing system, not merely as a privacy-preserving or attack-resistant training architecture, especially for regulated, cross-silo, and high-risk real-world deployments.
S. Mohammed, D. Alhadidi, A. Ngom· Journal of Cybersecurity and...· 0 citations