Mosaic Learning: A Framework for Decentralized Learning with Model Fragmentation

Decentralized learning (DL) enables collaborative machine learning (ML) without a central server, making it suitable for settings where training data cannot be centrally hosted. We introduce Mosaic Learning, a DL framework that decomposes models into fragments and disseminates them independently across the network. Fragmentation reduces redundant communication across correlated parameters and enables more diverse information propagation without increasing communication cost. We theoretically show that Mosaic Learning (i) shows state-of-the-art worst-case convergence rate, and (ii) leverages parameter correlation in an ML model, improving contraction by reducing the highest eigenvalue of a simplified system. We empirically evaluate Mosaic Learning on four learning tasks and observe up to 12 percentage points higher node-level test accuracy compared to epidemic learning (EL), a state-of-the-art baseline. In summary, Mosaic Learning improves DL performance without sacrificing its utility or efficiency, and positions itself as a new DL standard.

Low-Cost Privacy-Aware Decentralized Learning

This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that relies on adding correlated noise to each model update during the model training process. This technique ensures that the added noise almost neutralizes itself during the aggregation process due to its correlation, thus minimizing the impact on model accuracy. In addition, ZIP-DL does not require multiple communication rounds for noise cancellation, addressing the common trade-off between privacy protection and communication overhead. We provide theoretical guarantees for both convergence speed and privacy guarantees, thereby making ZIP-DL applicable to practical scenarios. Our extensive experimental study shows that ZIP-DL achieves the best trade-off between vulnerability and accuracy. In particular, ZIP-DL (i) reduces the effectiveness of a linkability attack by up to 52 points compared to baseline DL, and (ii) achieves up to 37 more accuracy points for the same vulnerability under membership inference attacks against a privacy-preserving competitor