Poisoning Attacks in Federated Learning: An Accountability- Oriented Survey with Centralized Learning as a Baseline

Abstract

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.