Privacy-preserving federated learning framework
Abstract
Federated Learning (FL) enables multiple parties to collaboratively train a shared model without exposing raw data. However, FL often suffers from degraded performance due to three core challenges: non-IID data distributions, residual privacy risks, and limited data volume per node. This work explores the privacy–performance trade-offs arising from these challenges and proposes a unified framework combining partial data sharing, anonymisation, and data augmentation. We first investigate how non-IID distributions impair standard FL convergence, and propose a Cluster-of-Trust (CoT) mechanism that groups mutually trusted clients for hierarchical aggregation. Experiments on the IoT-23 intrusion detection dataset show that CoT significantly improves model accuracy and convergence speed, closing most of the gap to centralised training. Next, we compare combinations of privacy-preserving techniques—including differential privacy, data anonymisation, and CoT-based grouping—highlighting how these impact model utility. Our results demonstrate that strong privacy guarantees can be achieved with only modest accuracy loss and reduced communication overhead. To address the limited data volume problem, we introduce data augmentation strategies and test their effectiveness in a privacy-sensitive Human Activity Recognition (HAR) task. We find that combining augmentation with trust-aware aggregation improves generalisation under strict privacy constraints. Across two domains—cybersecurity and…
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