Abstract
The Sixth-Generation (6G) network envisions pervasive artificial intelligence (AI) as a core goal, enabled by edge intelligence through on-device data utilization. To realize this vision, federated learning (FL) has emerged as a key paradigm for collaborative training across edge devices. However, the sensitivity and heterogeneity of edge data pose key challenges to FL: parameter sharing risks data reconstruction, and a unified global model struggles to adapt to diverse local distributions. In this paper, we propose a novel federated learning framework that integrates personalized differential privacy (DP) and adaptive model design. To protect training data, we leverage sample-level representations for knowledge sharing and apply a personalized DP strategy to resist reconstruction attacks. To ensure distribution-aware adaptation under privacy constraints, we develop a privacy-aware neural architecture search (NAS) algorithm that generates locally customized architectures and hyperparameters. To the best of our knowledge, this is the first personalized DP solution tailored for representation-based FL with theoretical convergence guarantees. Our scheme achieves strong privacy guarantees for training data while significantly outperforming state-of-the-art methods in model performance. Experiments on benchmark datasets such as CIFAR-10 and CIFAR-100 demonstrate that our scheme improves accuracy by 6.82\% over the federated NAS method PerFedRLNAS, while reducing model size to 1/10 and communication cost to 1/20.
Abstract (translated)
第六代（6G）网络将普及的人工智能视为核心目标，通过设备边缘智能和本地数据利用来实现。为了实现这一愿景，联合学习（FL）作为一种关键范式在边缘设备间的协作训练中崭露头角。然而，边缘数据的敏感性和异质性给FL带来了重大挑战：参数共享存在数据重建的风险，而统一的全局模型难以适应多样化的本地分布情况。为此，在本文中我们提出了一种新颖的联邦学习框架，该框架结合了个性化的差分隐私（DP）和自适应模型设计。为了保护训练数据，我们利用样本级别的表示来进行知识分享，并应用个性化DP策略以抵御重建攻击。在确保受隐私约束的情况下进行分布感知型适应方面，我们开发出了一种隐私感知的神经架构搜索（NAS）算法，该算法能够生成本地定制化的架构和超参数。 据我们所知，这是第一个针对基于表示的FL、具备理论收敛保证的个性化DP解决方案。我们的方案在提供强大训练数据隐私保护的同时，其模型性能显著超越了现有的最先进技术方法。我们在CIFAR-10和CIFAR-100等基准数据集上的实验表明：相较于联邦NAS方法PerFedRLNAS，我们的方案能够提高6.82%的准确性，并且将模型大小减少到原来的十分之一，通信成本降低至五分之一。
URL
https://arxiv.org/abs/2509.23030
