FedSDR augments federated self-distillation with dual LoRA streams (local smoothing and global rectification) to produce globally aligned, factually faithful models under statistical heterogeneity.
Proceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FedNL reformulates federated learning as nested optimization with linear attention for collaborative test-time adaptation on non-IID data.
citing papers explorer
-
FedSDR: Federated Self-Distillation with Rectification
FedSDR augments federated self-distillation with dual LoRA streams (local smoothing and global rectification) to produce globally aligned, factually faithful models under statistical heterogeneity.
-
Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
FedNL reformulates federated learning as nested optimization with linear attention for collaborative test-time adaptation on non-IID data.