Jul 2026· Fall Joint Computer Conference· pp. 185-192· 0 citations· 33 references
Abstract
Personalized Federated Learning (pFL) has emerged as a promising paradigm, while existing approaches face 3 limitations: granularity mismatch, resource waste, and conflict aggregation. This paper presents a Personalized Federated Learning with Low-rank Pruning-based Adaptation (pFedLoPA) framework. Instead of balancing global and local trade-offs, pFedLoPA decouples the model into client-specific cores and globally shared complements. It integrates low-rank adaptation to constrain optimization to a compact subspace, gradient-based pruning to identify personalized parameters, and complementary aggregation to exchange only relevant updates. This enables clients to retain critical knowledge locally while efficiently integrating global knowledge. Extensive experiments on CIFAR-10/100 with multiple network architectures demonstrate that pFedLoPA outperforms state-of-the-art methods in test accuracy (up to 94.31% on CIFAR-10) while reducing communication costs by over 70%.
Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a personalized aggregation set for each client.
Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay· 0 citations
This work proposes SeFoRA, a sketch-aggregated federated LoRA algorithm in which each client transmits a linear sketch of its local updates, enabling direct aggregation at the federator, and introduces a rank-homogeneous version called SeFoRA-Ho which allows for direct adapter aggregation in this setting.
Yue Xia, Tayyebeh Jahani-Nezhad, Mayank Bakshi et al.· 1 citation
This paper proposes Federated Learning with Consistency Optimization Algorithms (FedCO), a novel optimization framework that incorporates a label-skew-aware correction loss and neural feature distribution regularization during local training that significantly improves accuracy and convergence under diverse non-IID settings.
Ruiqi Wu, Yehong Li, Hongjie Guo et al.· Computers, Materials & C...· 0 citations
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.
A domain-sensitive federated pruning framework that preserves domain-invariant structures while retaining domain-specific representations and a structure-aware aggregation algorithm that fuses heterogeneous personalized architectures into a domain-generalized global model is proposed.
Chenchen Lin, Wenhao Yuan, Zhengji Xu et al.· 0 citations
A novel FL framework is presented, FedPhoenix, that stochastically re-sets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific overfitting features.
Jiahao Wu, Ming Hu, Yanxin Yang et al.· Advances in Neural Informati...· 1 citation