3 accepted papers
Federated learning (FL) on graphs shows promise for distributed time-series forecasting. Yet, existing methods rely on static topologies and struggle with client heterogeneity. We propose Fed-GAME, a framework that models personalized aggregation as message passing over a learnable dynamic implicit…
High gradient variance presents a significant obstacle to efficient post-training of large language models (LLMs) on memory-constrained devices. Existing practical strategies-such as reducing batch sizes or adopting gradient accumulation (GA)-suffer from an inherent trade-off: smaller batches exacer
Real-time License Plate Recognition plays a significant role in traffic congestion control and road safety monitoring. Practically, a network camera may capture multiple license plates in one frame while the data collected by different network cameras cannot be shared due to privacy concern. In this…