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heterogeneous-data

topic3 events
papersTODAY 04:00 UTC

Multi-source conformal prediction method uses localization to handle heterogeneous data

A new arXiv paper proposes a conformal prediction approach that draws on multiple heterogeneous data sources rather than treating them as one pool. The method exploits differences between sources through localization, aiming to keep prediction sets reliable when the test distribution departs from any single source. This targets settings where combining sources is useful but naive pooling would break coverage guarantees.

conformal-prediction/research/uncertainty/machine-learning
papersTODAY 04:00 UTC

Study Analyzes Convergence of Sequential Federated Learning on Heterogeneous Data

The paper compares two federated learning setups: parallel training, where clients work simultaneously, and sequential training, where clients update the model one after another. It derives convergence guarantees for the sequential approach when client data distributions differ, a setting where parallel methods often struggle. The analysis aims to clarify when sequential federated learning offers theoretical advantages over its parallel counterpart.

federated-learning/arxiv/optimization/research