FedADB, a Class Anchor-Driven Dual-Branch FL framework, a dual-branch collaborative training mechanism designed for clients that achieves significant improvements in both accuracy and convergence speed.
Abstract
Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for effective distributed modeling with data privacy preservation. However, local training suffers from the forgetting of previously learned global knowledge under cross-client data heterogeneity, which leads to significant declines in both performance and convergence speed. Most previous studies rely on global alignment strategies to retain global knowledge, which hinder local optimization and lead to inadequate supervision of missing classes. Some studies introduce proxy datasets to supplement supervision for missing classes. However, it remains a challenge to balance class-wise global consistency and local optimization objectives without proxy datasets. In this work, we propose FedADB, a Class Anchor-Driven Dual-Branch FL framework. Specifically, the server generates class anchors optimized in a differentiable input space, which are shared across clients. These class anchors serve as global references that provide supervision for missing classes during local training. A dual-branch collaborative training mechanism is designed for clients. In this mechanism, the anchor-based global branch focuses on learning with global consistency, achieving global knowledge alignment by class-anchor balanced sampling. The local calibration branch focuses on learning discriminative local features, mitigating the degradation of local representations caused by excessive global alignment. Extensive experiments across multiple medical and natural datasets demonstrate that FedADB achieves significant improvements in both accuracy and convergence speed.
FedTaste is proposed, a parameter-efficient framework for topology-aware structural transfer in Multimodal Federated Learning with missing modalities that avoids explicit modality imputation while preserving shared semantic structure across clients.
Federated Active Learning (FAL) addresses the dual challenges of data privacy and label scarcity, where the absence of a global data view introduces additional hurdles for coordinated query selection. We study cross-silo FAL in the low-budget regime, where annotation decisions are most critical. We characterize, both theoretically and empirically, a heterogeneity reversal: in low-budget settings, homogeneous (IID) data requires stronger coordination to avoid redundant queries, whereas heterogeneous data naturally promotes diversity; this trend reverses at higher budgets. Thus, in contrast to the standard federated learning (FL) narrative where heterogeneity is a primary challenge, we show that IID settings are more challenging for query selection in FAL. Motivated by these findings, we propose a new FAL framework that utilizes federated representation learning to align client data in a shared embedding space. This enables the server to perform globally coordinated active selection over optionally obfuscated client embeddings, while annotation remains local to each client. Although our framework operates in the more challenging low-budget regime, it achieves performance that surpasses existing FAL methods even when they are given substantially larger annotation budgets, demonstrating the value of centralized coordination under privacy constraints.
Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. This dissertation presents methodologies for building efficient personalized models by identifying which strategies are effective in the global training stage and by showing how to preserve global knowledge while securing user-specific performance during local adaptation. First, we show that as data heterogeneity increases, the collapse of feature vectors is a more fundamental bottleneck than classifier weights, and propose a method that directly mitigates the discrepancy in representation magnitude between local and global models. Second, we analyze that a training approach that strengthens local alignment can induce forgetting of global knowledge (e.g., categories not observed locally), and propose a method that achieves both local alignment and global knowledge preservation by combining feature distillation based on the global model's feature vectors. Third, in federated personalized reward model learning with preference heterogeneity, we empirically verify the conventional belief that"increasing the number of global models yields better initialization,"and we show that when sufficient local fine-tuning is allowed, a single global initialization can instead provide stronger personalization performance. This study redefines the role of global initialization under data and preference heterogeneity and provides practical training strategies that simultaneously satisfy global knowledge preservation and personalization.
This work proposes a data-Quality-aware aggregation framework by introducing an Evolutionary-computation-inspired de-sign into Federated learning ( FedEvoQ), with a lightweight dual-branch architecture.
Federated learning is an emerging paradigm that addresses privacy concerns by training on decentralized data, but it faces significant performance challenges due to factors like non-independent and identically distributed (non-IID) data and dynamic data distributions. These challenges are further amplified in federated class-incremental learning under realistic, non-monotonic class streams, where previously seen classes can intermittently reappear alongside new classes—making generative replay highly susceptible to training instability, mode collapse, and feature drift. To address these issues, we propose RAVEL-FCL, a generative replay-based framework for federated continual learning. Our approach integrates an improved generative model based on Rebooting ACGAN with multi-level feature alignment to ensure consistency, and employs Elastic Variational Continual Learning on the server to probabilistically regularize the global model and preserve past knowledge. Experimental evaluations show that our method achieves stable convergence and significantly outperforms state-of-the-art methods on standard benchmarks—for example, it improves accuracy by 5.27% over FedCIL on EMNIST-Balanced and by 1.60% and 1.27% over the strongest baselines on TinyImageNet and ImageNet-LT, respectively—while maintaining a forgetting rate below 5% across tasks. By jointly addressing feature drift and catastrophic forgetting, RAVEL-FCL provides a robust and privacy-preserving solution suitable for the adaptive and autonomous systems envisioned for Industry 5.0.
Yurui Zhou, Jia Hu, Geyong Min et al.· ACM Transactions on Autonomo...· 0 citations