The increase in privacy concerns among the users has led to edge based analytics applications such as federated learning which trains machine learning models in an iterative and collaborative fashion on the edge devices without sending the raw private data to the central cloud. In this paper, we propose a system for enabling iterative collaborative processing (ICP) in resource constrained edge environments. We first identify the unique systems challenges posed by ICP, which are not addressed by the existing distributed machine learning frameworks such as the parameter server. We then propose the system components necessary for ICP to work well in highly distributed edge environments. Based on this, we propose a system design for enabling such applications over the edge. We show the benefits of our proposed system components with a preliminary evaluation.
|Original language||English (US)|
|State||Published - 2019|
|Event||2nd USENIX Workshop on Hot Topics in Edge Computing, HotEdge 2019, co-located with USENIX ATC 2019 - Renton, United States|
Duration: Jul 9 2019 → …
|Conference||2nd USENIX Workshop on Hot Topics in Edge Computing, HotEdge 2019, co-located with USENIX ATC 2019|
|Period||7/9/19 → …|
Bibliographical noteFunding Information:
We thank the anonymous reviewers and our shepherd Bernard Wong, for many constructive comments and suggestions that greatly improved the quality of this paper. This work was sponsored in part by NSF under Grants CNS-1717834, CNS-1619254 and III-1422802.
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