Straggler-Aware Asynchronous Federated Learning via Temporal Cost Clustering for Fair and Efficient Heterogeneous Model Training

DASFL: Dynamic Adaptive Split Federated Learning for Heterogeneous Clients

FedBudget: A Budget-Aware Federated Learning Method for Communication-Constrained Distributed Data Mining

Federated learning enables collaborative data mining without centralizing raw data, but communication budgets remain a practical bottleneck in distributed deployment. Existing federated optimization methods mainly address statistical heterogeneity or aggregation stability, while client participation is often treated as full participation or random sampling. This paper proposes FedBudget, a budget-aware client selection method for communication-constrained federated data mining. In each round, FedBudget constructs a scheduling score from historical utility, stability, freshness, communication cost, and a coverage-aware penalty, and then greedily selects clients under a given communication budget. The aggregation stage follows the standard sample-size-weighted selected-client FedAvg rule, which makes the scheduling contribution directly attributable. Experiments on AI4I, Mammography, Shuttle, SMD, and SWaT compare FedBudget with representative federated optimization and scheduling baselines. Statistical analysis shows that FedBudget significantly reduces communication cost and improves communication-normalized performance relative to budgeted optimization baselines, while maintaining competitive AUC and PR-AUC. Larger-scale experiments with 20 and 50 simulated clients show mean performance-per-MB improvements of 4.019 and 1.945, respectively, together with lower mean communication cost. Sensitivity and convergence analyses confirm the robustness of the proposed scheduling mechanism and its stable communication–performance trade-off. These results indicate that explicit budget-aware participation modeling improves communication efficiency in federated data mining while preserving a simple and compatible training pipeline.

Federated Learning over 5G/6G Networks: Dynamic Client Selection and Resource Allocation for Heterogeneous Edge Environments

Results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks.

Stubbornness-Aware Peer-to-Peer Bayesian Federated Learning Over Graphs

Edge devices such as sensors, vehicles, and industrial controllers often need to learn a shared predictive model without pooling raw data or relying on a central server, even when local data quality varies widely across devices. This paper proposes a fully decentralized, peer-to-peer Bayesian federated learning method that explicitly regulates the relative influence of newly observed local data and neighbor information via an agent-specific stubbornness parameter. In each communication round, every device performs a stubbornness-weighted Bayesian-type local update using a fresh sample, exchanges the resulting intermediate belief with one-hop neighbors, and then fuses received beliefs through graph-weighted aggregation. To accommodate different training stages and heterogeneous data, we further introduce 1) time-varying schedule that gradually transitions from fast early adaptation to stable late-stage refinement, and 2) data-quality-aware rule that assigns agent-specific stubbornness using a simple similarity score. We provide a finite-time analysis that yields performance guarantees and clarifies how emphasizing higher-quality, lower-noise agents accelerates learning. Experiments on distributed linear regression and decentralized image classification demonstrate faster convergence than common decentralized baselines and improved robustness when some agents hold biased, scarce, or uninformative data.

Reliability-Aware Client Selection in Asynchronous and Heterogeneous Federated Learning

Polaris is a client selection framework for asynchronous and heterogeneous federated learning that employs quality-aware sampling to accelerate convergence. Its reliance on scalar update norms as quality indicators exposes it to exploitation, in which clients submit near-zero Gaussian updates that inflate the update magnitude proxy within the Polaris sampling objective without contributing meaningful gradient information. This work introduces a lightweight reliability-aware extension that embeds two server-side signals, Exponential Moving Average (EMA)-based magnitude credibility and cosine similarity-based directional alignment, directly into the staleness and aggregation weight proxy computation without modifying the geometric programming objective or aggregation semantics. Experiments across ResNet-18 on CIFAR-10, LeNet-5 on FEMNIST, and LeNet-5 on MNIST under non-IID data distributions confirm that attack effectiveness and defense discrimination are governed by task complexity and non-IID (non-Independent and Identically Distributed) degree. The proposed method demonstrates partial resistance to convergence collapse at lower adversarial ratios while introducing only a marginal accuracy overhead under clean conditions.

Toward Efficient Semi-Asynchronous Federated Learning: A Multi-Factor Grouping and Dual-Level Selection Scheme Under Heterogeneous Environments

Semi-Asynchronous Federated Learning (SAFL) takes advantage of both synchronous and asynchronous FLs to train models. However, existing works in semi-asynchronous FLs fail to fully account for heterogeneities in both data and devices. To address these issues, we first propose a Clustered SAFL (CSAFL) framework and theoretically analyze its convergence loss. Then, a convergence loss minimization problem is formulated under the considerations of heterogeneities in device resources, fairness of cluster selection, and data heterogeneity. To address this complex problem due to nonlinearities and multi-dimensional decision variables, we first design a device clustering algorithm based on both devices’ model parameter differences and gradient directions between local and global models. Then, the original loss minimization problem is transformed into inter- and intra-cluster selection problems. For the inter-cluster selection problem, to ensure fairness, we propose a reinforcement learning-driven Lyapunov approach to fairly select clusters, where reinforcement learning (RL) is used to supervise the cluster selection results by the Lyapunov method. For the intra-cluster selection, we convert it into a constrained multi-armed bandit (MAB) problem in order to let devices within a cluster submit models synchronously. Then, a two-stage Upper Confidence Bound (UCB) scheme is proposed to obtain device selection results. Extensive numerical results with baselines show that our approach achieves up to 25% higher accuracy.