FEAT mitigates representation collapse and prediction bias in federated continual learning by aligning feature angular similarities to shared Equiangular Tight Frame prototypes and removing task-irrelevant directional components from embeddings.
Federated class- incremental learning: A hybrid approach using latent exemplars and data-free techniques to address local and global forgetting
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
DLC inserts lightweight classifier-proximal plugins into distillation-based continual learning to achieve 8% accuracy gains on large benchmarks with only 4% extra backbone parameters.
citing papers explorer
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Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
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Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins
DLC inserts lightweight classifier-proximal plugins into distillation-based continual learning to achieve 8% accuracy gains on large benchmarks with only 4% extra backbone parameters.