实测发现现有联邦持续学习方法在资源受限下全失效，暴露出部署瓶颈。
Resource-Constrained Federated Continual Learning: What Does Matter?
- 构建大规模基准测试，评估主流FCL方法在存储、算力等限制下的表现
- 超过1000小时实验显示：资源受限时所有方法性能严重退化
- 揭示当前方法对资源依赖过强，为未来研究指明方向
联邦持续学习（FCL）旨在让边缘设备在不泄露隐私的前提下，持续学习不断到来的数据流，同时保留旧知识并适应新数据。现有研究多关注数据隐私和历史数据访问，却忽略训练开销限制。然而在真实场景中，边缘设备常受存储、计算预算和标签率等资源约束。本文通过大规模基准测试，分析六种典型数据集上两类增量学习场景（类增量与域增量）中主流FCL方法在不同资源限制下的表现。历时超过1000+ GPU小时的系统性实验表明：在资源受限条件下，所有现有FCL方法均无法达到预期性能，且敏感性分析结果一致。这说明当前多数FCL方法对资源过于依赖，难以实际部署。此外，我们还深入分析了典型技术在资源约束下的表现，为未来研究提供关键洞见。
原文摘要 · Abstract (English)
Federated Continual Learning (FCL) aims to enable sequentially privacy-preserving model training on streams of incoming data that vary in edge devices by preserving previous knowledge while adapting to new data. Current FCL literature focuses on restricted data privacy and access to previously seen data while imposing no constraints on the training overhead. This is unreasonable for FCL applications in real-world scenarios, where edge devices are primarily constrained by resources such as storage, computational budget, and label rate. We revisit this problem with a large-scale benchmark and analyze the performance of state-of-the-art FCL approaches under different resource-constrained settings. Various typical FCL techniques and six datasets in two incremental learning scenarios (Class-IL and Domain-IL) are involved in our experiments. Through extensive experiments amounting to a total of over 1,000+ GPU hours, we find that, under limited resource-constrained settings, existing FCL approaches, with no exception, fail to achieve the expected performance. Our conclusions are consistent in the sensitivity analysis. This suggests that most existing FCL methods are particularly too resource-dependent for real-world deployment. Moreover, we study the performance of typical FCL techniques with resource constraints and shed light on future research directions in FCL.
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