The paper demonstrates that sharing a deep encoder alone does not eliminate the confounding effects in task-comparison scores. By introducing a conditional two‑discriminator discrepancy within the embedding space, the authors achieve robust detection of task changes, maintaining stability under input rotations and accurately tracking label‑permutation drift. This approach, integrated into a mixture‑of‑heads framework, outperforms traditional novelty triggers and generalizes across multiple backbones and datasets, including ImageNet‑21k ViT‑B/16, DINOv2, and CIFAR‑100.
By Kentaro Oda
ORDERS is a personalized federated learning configuration that combines a shared backbone, private residual adapters, and a classifier, using geometric weights based on descending update norms, feature alignment, and private‑parameter perturbations. In extensive experiments on CIFAR‑10 and Sent140, ORDERS achieves slightly higher mean client accuracy than baseline methods, with a 5.47% reduction in parameter payload. Ablation studies show limited, dataset‑dependent benefits from norm ranking and alignment, and no clear advantage from perturbations.
By Koffka Khan
The paper introduces a controlled evaluation framework to determine when encoder adaptation is beneficial after correcting for class imbalance in long‑tailed recognition tasks. By separating unique support from representation allocation and comparing classifier‑only versus full‑network continuations, the study shows that encoder updates become worthwhile only when representation allocation is highly imbalanced, a transition that persists across various corrections and scales to ImageNet. The framework uses stage‑specific controls, multiple objectives, whole‑distribution metrics, and hierarchical inference to robustly estimate incremental encoder value.
By Zexing Zhang, Jichao Li, Tianyang Lei, Yiyin Zhang, Kewei Yang
arXiv:2611.04667v2 Announce Type: replace
Abstract: Federated Learning (FL) enables collaborative training of machine learning models across distributed clients while preserving data privacy. In prac...
By Ghani Haider, Majid Kundroo, Leo Zhang, Jinchul Choi, Taehong Kim
The paper investigates drift detection in deep learning models, showing that sharing a deep encoder alone does not eliminate confounding in task-comparison scores. By introducing a conditional two‑discriminator discrepancy into the embedding space, the authors create a two‑axis gate that remains stable under input rotations and accurately tracks label‑permutation drift. This approach outperforms traditional exchange or novelty triggers, achieving high AUROC in distinguishing semantic novelty from photometric shift across multiple backbones and datasets.
By Kentaro Oda
arXiv:2606. 10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance.
By Haengbok Chung, Jae Sung Lee
The paper introduces Joint Domain-Class Federated Learning (JDFL), a lightweight, optimizer‑agnostic extension designed to address feature skew in federated learning. JDFL infers pseudo‑domains from local update signals and expands the classifier head to output joint domain‑class logits, enabling the model to capture domain‑conditioned appearance while sharing a backbone. Two supervision strategies—similarity‑aware soft‑labeling and per‑sample randomized target assignment—are proposed to train the expanded head, and experiments on domain‑shifted image benchmarks show consistent improvements in global test accuracy over standard FL methods.
By Sina Najafi, Mostafa Tavassolipour, Seyed Pooya Shariatpanahi
The paper introduces FedPFT, a federated learning framework that tackles the feature‑classifier mismatch problem by using personalized prompts processed through a shared self‑attention transformation module. Unlike prior methods that either degrade the feature extractor or address the mismatch only after training, FedPFT aligns local features with the global classifier during training, improving aggregation and model performance. Experiments demonstrate that FedPFT surpasses state‑of‑the‑art methods by up to 5.07%, and gains up to 7.08% when combined with collaborative contrastive learning.
By Xinghao Wu, Xuefeng Liu, Jianwei Niu, Guogang Zhu, Mingjia Shi, Shaojie Tang, Jing Yuan
arXiv:2506. 22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL).
By Randeep Bhatia, Nikos Papadis, Murali Kodialam, TV Lakshman, Sayak Chakrabarty
arXiv:2610.38970v2 Announce Type: replace
Abstract: Current and emerging mobile networks face increasing privacy, scalability, and performance challenges as distributed learning becomes essential for...
By Martin Isaksson, Rickard C\"oster, \v{S}ar\=unas Girdzijauskas, Seif Haridi
Ampere is a new split federated learning system that reduces both on‑device computation and device‑server communication while improving accuracy. It trains device and server blocks sequentially with local losses, eliminating gradient transfers, and uses a lightweight auxiliary network to consolidate activations into a single transfer. Experiments on CNNs and Transformers show up to 11.70 pp accuracy gains, 18.6× faster training, 911× less communication, and 14.5× less computation compared to state‑of‑the‑art SFL baselines.
By Zihan Zhang, Leon Wong, Blesson Varghese
FedSAP is a federated learning framework that addresses heterogeneous edge devices by using structured pruning as a budget-constrained tri-state channel allocation. It partitions model channels into a Global pool, pseudo-domain-specific Private pools, and a Dropped state, allowing broadly useful features to be shared while isolating domain-sensitive updates. Experiments on Digits and Office-Caltech datasets show FedSAP achieving higher mean global accuracy than the strongest baseline while supporting up to 80% client pruning ratios.
By Wentao Yue, Tianyou Lai, Hongji Li, Qingyu Mao, Qilei Li