Revisiting Differentially Private Federated Learning for Tabular Data: A Matched-Accounting Benchmark of Boosting Versus DP-SGD

Benchmarking Optimiser Choice and Privacy-Risk Dynamics in Non-IID Federated Learning

Federated Learning (FL) trains shared models across distributed clients without pooling raw data, but its behaviour under heterogeneous data distributions and the associated privacy risks remain imperfectly understood. This paper presents a controlled empirical comparison of Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Adaptive Moment Estimation (FedAdam), and Differentially Private Federated Averaging (DP-FedAvg) under matched conditions. The experiments use CIFAR-10 and MNIST with Non-Independent and Identically Distributed (non-IID) client partitions, including Dirichlet and class-restricted partitioning. Each condition is repeated across 20 independent seeds. We examine how optimisation choice shapes convergence and stability, how differential privacy affects classification performance, and how privacy exposure changes over the course of training. We find that FedAdam improves performance under severe client heterogeneity, where client gradients conflict strongly. FedProx changes the training trajectory, but we do not find it to improve cross-seed stability. DP-FedAvg reduces the success of gradient-reconstruction and membership-inference attacks, but this protection comes with a measurable reduction in classification performance due to gradient clipping and additive noise. A key finding is that privacy exposure is temporally concentrated rather than uniform across training. It is highest in the earliest rounds, when gradients are largest and most informative, and declines as training progresses. These results suggest that private FL systems should account for training dynamics, with particular emphasis on protection during early training rounds.

Federated Learning with Differential Privacy for Fraud Detection: Evaluating Performance Under IID and Non-IID Data Distributions

Investigating Privacy-Preserving Federated Learning for Telecom Customer Churn Prediction Using Differential Privacy

Predicting customer churn in the telecom sector is critical for retaining subscribers, maintaining brand reputation, and staying ahead of competitors. Losing customers not only reduces revenue but can also weaken long-term market position in a highly competitive industry. While machine learning has been widely used to address this challenge, most traditional approaches depend on centralizing customer data. This raises major concerns about user privacy, data ownership, and compliance with strict regulations such as GDPR. These challenges make it difficult for businesses to fully utilize customer data while safeguarding sensitive information. In this paper, we investigate a privacy-preserving approach to churn prediction that combines federated learning (FL) with differential privacy (DP). Rather than collecting all customer data in a single repository, the investigated framework enables multiple clients to collaboratively train a deep neural network while maintaining data locality during the federated training process. To further enhance privacy protection, we employ Differentially Private Stochastic Gradient Descent (DP-SGD) and add controlled noise to model updates, reducing the possibility of inferring individual data contributions. This work systematically evaluates how different privacy levels, expressed through ε and δ, influence model performance under simulated non-IID client distributions. The experiments analyze the privacy–utility trade-off using multiple evaluation metrics and compare the results with centralized and non-private federated-learning approaches. The findings show that the investigated framework maintains competitive predictive performance across a range of privacy budgets while demonstrating a clear privacy–utility trade-off. Very strict privacy budgets result in substantial performance degradation, particularly for smaller and more imbalanced datasets, whereas moderate privacy budgets maintain competitive predictive performance with limited degradation. This study highlights the potential of privacy-preserving federated learning for practical distributed analytics applications where protecting sensitive data is essential.

Federated Learning with Differential Privacy: A Comprehensive Framework for Privacy-Preserving Distributed Machine Learning

Overview: Federated Learning (FL) is a type of machine learning in which multiple clients use their own data to train a model. This does not require sending the raw data from each client to a central server. However, FL is currently very vulnerable to several types of attacks, including inference, model inversion, and membership inference. In this study, we have implemented a comprehensive experimental framework for analysing FL performance using standard FL aggregation protocols FedAvg, FedProx, and SCAFFOLD in conjunction with Differential Privacy (DP) mechanisms; specifically, the Gaussian noise mechanism with Rényi Differential Privacy (RDP) accountants. Our experiments all used K = 100 simulated clients, with heterogeneous data distribution across the entire federation (non-IID), and the respective clean datasets used were MNIST and CIFAR-10. For example, in our IID scenarios, our DP-FedAvg protocol achieved an accuracy of 84.9% on MNIST with ε = 1.0 and δ = 10^-5 (i.e., no privacy guarantees and 92.6% reuse of the same parameters). This corresponds to a 7.7-percentage-point trade-off in accuracy for provable privacy. In our non-IID scenario, we achieved an accuracy of 72.8% for the exact same privacy parameters and an LDA αᴸᴸᴸ = 0.5. We systematically analyse how various combinations of clipping norms on gradients, noise multipliers, and client participation rates converged. All results have been reported as-is, along with the associated standard deviation across each of the five independent sample runs that we performed using different random seed values. Finally, our framework will assist practitioners deploying privacy-friendly FL within either healthcare, banking, or Internet of Things environments.

Optimal Metric-Aware Class Rebalancing in Federated Learning

Class imbalance is a significant challenge in many practical classification tasks, particularly in federated learning (FL), where both global data imbalance and local data heterogeneity across clients worsen the problem. Addressing the class imbalance problem effectively while adhering to privacy constraints remains a formidable challenge. A common solution is to rebalance (or reweight) the samples of different classes; however, the rebalancing ratio largely depends on empirical results. In this study, we propose an analytical class rebalancing method to compute the optimal rebalancing ratios for imbalanced datasets. We first theoretically derive the relationship between evaluation metrics--such as macro-precision, macro-recall, and macro-F1--and the rebalancing ratio. Based on these findings, we devise an efficient algorithm to determine the optimal rebalancing ratio that maximizes the corresponding metrics. Our method is parameter-free and doesn't increase the complexity of existing neural models. We demonstrate that our method achieves the optimum ratio for maximizing the concerned metrics while maintaining low computational complexity, scaling linearly with both the number of clients and samples. Experimental results on different datasets validate the effectiveness of our algorithms. To the best of our knowledge, we are the first to provide an analytical and parameter-free solution to the problem.

FedMark-FM: Auditable, Risk-Adjusted Data Markets for Federated Foundation-Model Adaptation

Federated foundation-model adaptation increasingly relies on heterogeneous private artifacts (retrieval corpora, prompts and demonstrations, LoRA adapters, preference and safety data, and update sketches), yet existing federated-learning incentive mechanisms price clients as homogeneous data or update providers. This assumption poorly matches foundation-model pipelines, where contribution value is heterogeneous, non-IID, pipeline-dependent, privacy-constrained, and vulnerable to strategic behavior. We propose FedMark-FM, an auditable, risk-adjusted data-market framework that models clients as sellers of typed artifacts, estimates marginal contribution with S3Val, a stratified, uncertainty-aware Shapley estimator supporting pipeline-ordered valuation, and converts lower-confidence-bound values into budget-feasible payments penalizing duplication, sybil splitting, poisoned adapters, privacy-budget gaming, and cost inflation. We evaluate FedMark-FM-Bench across FEVER retrieval, held-out generator-backed RAG, and trained PEFT/LoRA tracks. Under a held-out prompt-injection poisoner, FedMark-FM improves downstream accuracy by 7.5-8.1 points over volume, leave-one-out, and FL-Shapley while selecting zero strategic clients. Split-conformal calibration reaches full lower-bound coverage at mean width 0.0141, versus 0.33 for naive intervals. We prove pipeline-ordered valuation is the unique credit rule respecting serving causality, and show it materially changes credit assignment (Spearman 0.76, selected-set overlap 0.67) while leaving held-out task quality unchanged; the market preserves rare specialists with audit-ready ledgers at 200-1000-client scale. FedMark-FM shows incentives for federated foundation models can be engineered as auditable data infrastructure coupling valuation, mechanism design, privacy interfaces, and pipeline-order semantics.