研究联邦平均在动作识别中个性化与泛化间的权衡。
FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracy
- 用FedAvg算法在客户端本地训练，平衡个性化与全局性能。
- 数据分布不均时，个性化效果下降，泛化能力减弱。
- 适合关注隐私保护下模型性能优化的研究者。
联邦学习（FL）通过将模型训练分布在客户端设备上，并仅共享参数，实现了分布式智能计算，有效提升了数据利用效率并缓解了隐私问题。然而，如何在模型个性化与泛化能力之间取得平衡仍是挑战。本文设计并实施多个测试场景，评估中心化、本地化及联邦学习范式的表现，并模拟客户端数据变化情况。实验基于人类活动识别（HAR）领域，应用FedAvg算法分析个性化与泛化准确率的权衡。结果表明，尽管FedAvg在常规条件下兼具较高个性化和泛化性能，优于传统中心化学习，但在客户端类别分布变化等压力条件下，该优势不再明显。
原文摘要 · Abstract (English)
The federated learning (FL) paradigm fosters distributed pervasive computing combined with artificial intelligence techniques, allowing for optimized data usage and improved mitigation of privacy concerns. Indeed, model training occurs on the client's local devices, and model parameters are subsequently shared with a centralized server. However, there is a need to find a tradeoff between models' personalization and generalization capabilities. In this paper, we design and implement several testing scenarios devoted to evaluating and comparing the centralized, local, and federated paradigm performances. We also design and implement a scenario that emulates a change in clients' data. We then present experimental results of the FedAvg algorithm applied to the Human Activity Recognition (HAR) domain to understand the trade-off between personalized and generalized accuracy. Results show that, although FedAvg confirms a higher degree of personalization capabilities while keeping a high degree of generalization with respect to the traditional centralized learning, this result is not so obvious under stressful conditions, such as when varying class distribution over clients.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。