heterogeneous-data

Setoka is a benchmark for evaluating memory-augmented personalized agents' ability to understand users hierarchically (semantic memory, episodic memory, behavior patterns, personality traits) from heterogeneous data, revealing that current memory systems struggle with tasks requiring cross-source integration and abstraction.

This paper introduces FedeKD, a reliability-aware framework for federated knowledge distillation that uses an energy-based gating mechanism to mitigate negative transfer in heterogeneous settings. The authors demonstrate that weighting knowledge transfer based on sample-wise trust improves robustness and predictive performance without requiring public datasets.