FedReLa: Re-labeling approach for imbalanced federated learning under data heterogeneity
Researchers propose FedReLa, a data-level method for federated learning that addresses the coexistence of global class imbalance and cross-client data heterogeneity. The approach uses a feature-dependent label re-allocator to correct biased global decision boundaries without requiring knowledge of the global class distribution. FedReLa is model-agnostic and modular, integrating with existing algorithmic methods without additional communication overhead, and claims state-of-the-art results on stepwise-imbalanced and long-tailed datasets.
Related events (8)
FedLAB: Traceable semantic codebooks for federated multimodal graph foundation learning
FedLAB is a new federated learning framework for multimodal graph foundation models that organizes knowledge into typed hierarchical codebooks covering modality evidence, node semantics, and topology context. The system enables semantic traceability under strict data isolation, addressing a gap where existing methods exchange knowledge through parameters or embeddings without exposing how evidence jointly supports predictions. Evaluated on 10 benchmarks and 6 downstream tasks, FedLAB improves over state-of-the-art baselines by up to 7.53% while keeping raw data local.
IntraShuffler: Privacy-Preserving Framework for Heterogeneous DP Federated Learning
This paper identifies a novel Privacy Inference Attack against heterogeneous differential privacy federated learning (HDP-FL) systems, where an honest-but-curious server exploits epsilon-aware aggregation and gradient denoising to infer client data distributions and link updates across rounds. To counter this, the authors propose IntraShuffler, a middleware framework that groups clients into privacy-compatible buckets and performs parameter-level shuffling within buckets, preserving epsilon-aware aggregation while disrupting persistent gradient structure. Experiments on four datasets show IntraShuffler reduces gradient recoverability by over 60% and drops surrogate inference accuracy from 0.78 to 0.33 with minimal utility loss.
FlashbackCL extends federated learning to mitigate temporal distribution shift and forgetting
FlashbackCL is a proposed extension to the Flashback federated learning method that addresses temporal forgetting — the degradation caused by client data distributions drifting over time, a scenario existing FL methods do not handle. The approach introduces temporally-decayed label counts, a device-aware replay buffer with Class-Balanced Reservoir Sampling, and server-side coreset curation. On CIFAR-10 with 50 clients, FlashbackCL achieves 6.9–10.0% relative improvement over Flashback while reducing temporal forgetting by up to 68%, with CBRS replay identified as the critical component.
Range Penalization for Federated Learning: Polar Clustering and Statistical Accuracy
This paper introduces range regularization for federated learning, identifying shared-weight features across clients while adaptively clustering personalized feature weights at extreme values (termed polar clustering). The approach targets statistical accuracy, cross-client regularity, and resource efficiency for quantization and coding. New nonasymptotic proof techniques are developed for the seminorm-based regularizer, alongside a fast optimization algorithm exploiting local strong convexity.
Federated Learning using Hugging Face and Flower
This Hugging Face blog post describes how to combine the Hugging Face ecosystem with the Flower federated learning framework to train models across distributed, privacy-preserving data silos. It provides a practical walkthrough of integrating Transformers and Datasets libraries with Flower's federated training loop. The post targets practitioners looking to apply federated learning to NLP and other ML tasks without centralizing sensitive data.
Can Foundation Models Label Data Like Humans?
This Hugging Face blog post examines whether foundation models can serve as substitutes for human annotators in RLHF data labeling pipelines. It investigates the reliability and quality of model-generated preference labels compared to human-generated ones, with implications for scalable oversight and alignment research. The analysis is framed around the Open LLM Leaderboard and RLHF methodology.
TREAD: VLM-based re-labelling framework improves robot policy generalization via dataset augmentation
TREAD (Task Robustness via Re-Labelling Vision-Action Robot Data) is a scalable framework that uses pretrained Vision-Language Models to augment existing robotics datasets without new data collection. The approach decomposes demonstrations into sub-tasks, segments videos accordingly, and generates linguistically diverse instruction labels, enriching language-action pair diversity. Evaluations on the LIBERO benchmark show improved generalization to novel tasks and goals, addressing a key limitation of current robot learning policies.
FINO: Label-free adaptation of vision foundation models using metadata in scientific domains
Researchers propose FINO, a self-supervised method for adapting vision foundation models to specialized scientific domains without task labels, using metadata as a guidance signal instead. The approach combines a standard self-supervised objective with flexible handling of both discrete and continuous metadata to preserve informative factors while suppressing spurious ones. Evaluated across subcellular fluorescence microscopy, Earth observation, wildlife monitoring, and medical imaging, FINO outperforms both unsupervised domain adaptation and fully supervised fine-tuning, including domain-specific state-of-the-art models.