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cs.LG2025
FedAPM: Federated Learning via ADMM with Partial Model Personalization
Shengkun Zhu, Feiteng Nie, Jinshan Zeng +6
In federated learning (FL), the assumption that datasets from different devices are independent and identically distributed (i.i.d.) often does not hold due to user differences, an…
cs.LG2024
On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means
Yushuai Ji, Zepeng Liu, Sheng Wang +2
The k-means algorithm can simplify large-scale spatial vectors, such as 2D geo-locations and 3D point clouds, to support fast analytics and learning. However, when processing large…