arXiv:2506.19260v2 Announce Type: replace-cross Abstract: Federated learning transmits only model updates to protect client data, and differentially private SGD (DP-SGD) bounds content-level leakage through those updates. Neither mechanism accounts for what the communication topology of the federation itself reveals. In cross-silo deployments, a passive adversary with knowledge of the topology and organisational structure has access to information channels that DP-SGD leaves entirely unaddressed. We formalise this threat and derive a principled defense. We introduce TADI (Topology-Aware Distri
The increasing adoption of federated learning in sensitive applications and cross-silo deployments highlights the urgent need to address overlooked privacy vulnerabilities inherent in communication topologies.
This research reveals a critical blind spot in current privacy safeguards for federated learning, demonstrating that even with differential privacy, network topology can leak sensitive information, requiring a fundamental rethink of security architectures.
The understanding of 'private' federated learning shifts from solely content-level protection to encompassing the underlying communication structure, demanding new defenses and architectural considerations for secure AI deployments.
- · Privacy-focused AI research institutions
- · Organizations implementing federated learning in highly sensitive sectors
- · Providers of secure multi-party computation and privacy-enhancing technologies
- · Adversaries exploiting network topology for data leakage
- · Current federated learning systems that do not account for topology-aware privac
- · Organizations relying solely on DP-SGD for complete privacy
This will lead to the development and integration of topology-aware privacy mechanisms into standard federated learning frameworks.
Increased trust in federated learning deployments for highly sensitive data, potentially accelerating adoption in sectors like healthcare and finance.
New regulatory requirements may emerge, mandating topology-aware privacy considerations for AI systems handling confidential information.
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