Sep 2026· IEEE Transactions on Mobile Computing· Vol 25, pp. 13413-13427· 0 citations· 61 references
Abstract
Federated Learning (FL) allows edge clients to collaborate in model training with data privacy preserved, yet it is known to suffer low training efficiency and model accuracy. Given that efficiency and accuracy are usually conflicting objectives, existing practices increasingly employ an adaptive scheme that changes the FL configurations (e.g., quantization or sparsification level) based on runtime training status, for which accurate training diagnosis—used for guiding the optimization actions—is crucial. However, while training diagnosis is a common task shared by different optimization schemes, existing works propose their diagnosis methods in an ad-hoc manner, which yield multiple limitations. First, the diagnosis metric in an optimization scheme may sometimes be less accurate than others; second, existing schemes fail to fully exploit the diagnosis result by applying it for only one optimization action; third, existing methods usually do not perceive cross-client data heterogeneity, failing to simultaneously enhance FL accuracy. To tackle those limitations, we make a systematical study on the training diagnosis methods of multiple optimization schemes, and propose metric grafting—replacing a scheme’s diagnosis metric with a better one to improve the training performance. Moreover, to fully exploit the potential of training diagnosis, we build a system platform that supports flexible combinations of training diagnosis and optimization actions (i.e., single-diagnosis-multiple-actions and multiple-diagnosis-multiple-actions). Evaluation on testbeds show that, with metric grafting and advanced diagnosis-action combinations, we can substantially improve the efficiency and accuracy performance of FL.
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.
Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a model while keeping their data localized, thus preserving privacy. While most FL approaches focus on deep learning models, there is a growing interest in exploring alternative models that can offer interpretability and efficiency. This paper presents TreeClimber, a novel neuro-symbolic approach for training decision tree models in a federated setting using gradient-free optimization techniques. TreeClimber embeds data into a subsymbolic latent space via a randomized embedding, from which decision trees are reconstructed through a neural architecture. Trees are optimized in the embedding space in a federated setting using a zeroth-order approximation of the gradient, allowing for effective training of interpretable decision models without direct access to data. Experimental results demonstrate the effectiveness of TreeClimber in enabling secure distributed training of decision trees without sharing raw information about local datasets, achieving competitive performance compared to traditional centralized training methods.
Andrea Augello, Stefano Merendino, Alessandra De Paola et al.· International Conference on...· 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
An adaptive FL framework that employs a hybrid contribution evaluation mechanism as the core principle for client selection and resource management is proposed and demonstrates that the proposed framework outperforms existing FL baselines in terms of training efficiency, data utilization, and fairness.
Younghwan Jeong, Sangshin Lee, Jinyoung Lee et al.· Scientific Reports· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
