arXiv subjects

Hangyu Ye

Publications and source records attributed to Hangyu Ye.

3 recordsLinked to original sources

arXiv2025

High-Energy Concentration for Federated Learning in Frequency Domain

Federated Learning (FL) presents significant potential for collaborative optimization without data sharing. Since synthetic data is sent to the server, leveraging the popular concept of dataset distillation, this FL framework protects real data privacy while alleviating data heterogeneity. However, such methods are still challenged by the redundant information and noise in entire spatial-domain designs, which inevitably increases the communication burden. In this paper, we propose a novel Frequency-Domain aware FL method with high-energy concentration (FedFD) to address this problem. Our FedFD is inspired by the discovery that the discrete cosine transform predominantly distributes energy to specific regions, referred to as high-energy concentration. The principle behind FedFD is that low-energy like high-frequency components usually contain redundant information and noise, thus filtering them helps reduce communication costs and optimize performance. Our FedFD is mathematically formulated to preserve the low-frequency components using a binary mask, facilitating an optimal solution through frequency-domain distribution alignment. In particular, real data-driven synthetic classification is imposed into the loss to enhance the quality of the low-frequency components. On five image and speech datasets, FedFD achieves superior performance than state-of-the-art methods while reducing communication costs. For example, on the CIFAR-10 dataset with Dirichlet coefficient $α= 0.01$, FedFD achieves a minimum reduction of 37.78\% in the communication cost, while attaining a 10.88\% performance gain.

cs.LG↗

arXiv2024

FedFQ: Federated Learning with Fine-Grained Quantization

Federated learning (FL) is a decentralized approach, enabling multiple participants to collaboratively train a model while ensuring the protection of data privacy. The transmission of updates from numerous edge clusters to the server creates a significant communication bottleneck in FL. Quantization is an effective compression technology, showcasing immense potential in addressing this bottleneck problem. The Non-IID nature of FL renders it sensitive to quantization. Existing quantized FL frameworks inadequately balance high compression ratios and superior convergence performance by roughly employing a uniform quantization bit-width on the client-side. In this work, we propose a communication-efficient FL algorithm with a fine-grained adaptive quantization strategy (FedFQ). FedFQ addresses the trade-off between achieving high communication compression ratios and maintaining superior convergence performance by introducing parameter-level quantization. Specifically, we have designed a Constraint-Guided Simulated Annealing algorithm to determine specific quantization schemes. We derive the convergence of FedFQ, demonstrating its superior convergence performance compared to existing quantized FL algorithms. We conducted extensive experiments on multiple benchmarks and demonstrated that, while maintaining lossless performance, FedFQ achieves a compression ratio of 27 times to 63 times compared to the baseline experiment.

cs.DC↗

arXiv2024

Permutation Equivariance of Transformers and Its Applications

Revolutionizing the field of deep learning, Transformer-based models have achieved remarkable performance in many tasks. Recent research has recognized these models are robust to shuffling but are limited to inter-token permutation in the forward propagation. In this work, we propose our definition of permutation equivariance, a broader concept covering both inter- and intra- token permutation in the forward and backward propagation of neural networks. We rigorously proved that such permutation equivariance property can be satisfied on most vanilla Transformer-based models with almost no adaptation. We examine the property over a range of state-of-the-art models including ViT, Bert, GPT, and others, with experimental validations. Further, as a proof-of-concept, we explore how real-world applications including privacy-enhancing split learning, and model authorization, could exploit the permutation equivariance property, which implicates wider, intriguing application scenarios.

cs.CR↗