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Introduces HERO, a heterogeneity-aware benchmark library for federated continual learning that separates task splits, client data splits, and client task sequences to enable reproducible and setting-aware evaluation.
This paper analyzes the effect of structural and temporal heterogeneities in decentralized federated learning over temporal networks, showing that ignoring these heterogeneities leads to unrealistically rapid convergence and that real-world networks slow down diffusion.
This paper reanalyzes a prior study claiming lower AI literacy predicts greater AI receptivity, finding that the aggregate negative relationship masks heterogeneity: the effect is not significant for text AI tools but remains strong for non-text AI tools, indicating a narrower pattern of broader adoption rather than general receptivity.