Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 194-199· 0 citations· 21 references
Abstract
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.
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 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
A thorough comparison between two well-known federated optimization algorithms, FedAvg and FedProx, and three popular deep convolutional neural network architectures such as ResNet18, VGG16 and VGG19 demonstrates that enforcing strong federated optimization coupled with fitting the appropriate deep convolutional architectures could provide a more reliable way of learning in decentralized settings.
Gitanjali Yadav, Jayashree V. Bagade· Journal of Intelligent Decis...· 0 citations
This study implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants.
A federated learning framework based on self-excluding aggregation (FedSEA) is proposed by integrating dynamic sparsity-driven client grouping, self-excluding aggregation and cross-group knowledge distillation, which achieves superior performance on the Office-Home and CIFAR-10 datasets.
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