A Decoupled Contrastive Learning Framework for Backdoor Defense in Federated Learning

Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy by sharing only local parameters. However, this decentralized setup, while preserving data privacy, also introduces new vulnerabilities, particularly to backdoor attacks, in which compromised clients inject poisoned data or gradients to manipulate the global model. Existing defenses rely on the global server to inspect model parameters, while mitigating backdoor effects locally remains underexplored. To address this, we propose a decoupled contrastive learning–based defense. We first train a backdoor model using poisoned data, then extract intermediate features from both the local and backdoor models, and apply a contrastive objective to reduce their similarity, encouraging the local model to focus on clean patterns and suppress backdoor behaviors. Crucially, we leverage an implicit symmetry between clean and poisoned representations—structurally similar but semantically different. Disrupting this symmetry helps disentangle benign and malicious components. Our approach requires no prior attack knowledge or clean validation data, making it suitable for practical FL deployments.