DEBUGGING IN FEDERATED LEARNING SYSTEMS

    公开(公告)号:US20240378455A1

    公开(公告)日:2024-11-14

    申请号:US18196062

    申请日:2023-05-11

    Abstract: In one embodiment, a device makes a determination that performance of a global model generated by aggregating local models trained by a plurality of trainer nodes in a federated learning system has experienced a degradation. The device selects, in response to the determination, a particular trainer node from among the plurality of trainer nodes to generate debugging metrics. The device provides an indication that the particular trainer node is a root cause of the degradation.

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