分布式学习即服务：开发者的视角
Distributed Learning as a Service: The Developer's Perspective
- National and Kapodistrian University of Athens（雅典国立卡波季斯特里安大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文从开发者视角展示分布式学习即服务（DLaaS），通过管理仪表板声明式启用差分隐私、分割学习、分层聚合和知识蒸馏，无需修改客户端代码，并在智能家居唤醒词任务上验证完整服务生命周期，实现实时设备端检测。
AI中文摘要：
分布式学习服务的应用开发者面临着典型的联邦学习循环未解决的挑战。具体而言，模型更新仍可能泄露私有数据，设备可能因资源有限而无法参与训练，单一的聚合器可能无法扩展，且模型权重的传输会产生可观的带宽成本。本文从开发者的视角展示了DLaaS（分布式学习即服务）。通过单一的管理仪表板，开发者启动分布式/联邦学习任务，并能够将差分隐私（DP）、分割学习（SL）、分层聚合（HA）和知识蒸馏（KD）作为声明式选项激活，而无需更改客户端代码。我们在工业智能家居唤醒词（WuW）任务上演示了完整的服务生命周期，使用了“Ok Aura”数据集。一旦开发者在管理仪表板中通过切换DP、SL、HA和KD来启动分布式学习任务，系统便将任务分派给一组Android客户端和Docker化的辅助聚合器。在演示中，这些机制在不同配置下实时运行。然后，客户端在本地训练模型并返回其更新。训练好的模型被提供给消费者端的Android应用程序，该应用程序在实时麦克风流上执行设备端WuW检测。特别是，会议与会者将被邀请说出触发短语，并实时监控每类置信度和推理延迟。最后，我们发布了这些配置的源代码和短视频演示。
英文摘要：
Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model updates can still leak private data, devices might not be able to participate in the training due to limited resources, a single aggregator might not be able to scale, and the transmissions of model weights induce a considerable bandwidth cost. This paper demonstrates DLaaS (Distributed Learning as a Service) from the developer's vantage point. Using a single admin dashboard, the developer initiates a distributed/federated learning job and is able to activate Differential Privacy (DP), Split Learning (SL), Hierarchical Aggregation (HA), and Knowledge Distillation (KD) as declarative options, with no change to the clients' code. We demonstrate the complete service lifecycle on an industrial smart-home Wake-up Word (WuW) task, using the "Ok Aura" dataset. Once the developer initiates a distributed learning job by toggling DP, SL, HA, and KD in the admin dashboard, the system dispatches the job to a set of Android clients and Dockerized helper aggregators. In the demonstration, these mechanisms run live across configurations. Then, the clients train the model locally and return their updates. The trained model is served to a consumer-side Android application that performs on-device WuW detection on a live microphone stream. In particular, the conference attendees will be invited to speak the trigger phrase and monitor in real time the per-class confidence and inference latency. Finally, we release the source code and short video walkthroughs of these configurations.
