Drift-Aware Straggler Mitigation for Heterogeneous Split Federated Learning via Proactive Cut-Layer and Asynchronous Server Updates

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

Heterogeneous federated learning leads to system and data differentials that cause stragglers to either be a bottleneck to synchronous optimization or create representation bias in asynchronous contexts. Although current approaches deal with staleness or buffering independently, their approach does not ensure fast clients do not take over the global model. The proposed framework Straggler-Aware Asynchronous Federated Learning (SAFL), that re-defines the stragglers as structured subjects rather than outliers. SAFL employs temporal exponentially weighted moving average signature of client costs and costs model updates by clustering costs in time-constrained per-cluster buffers. An innovative fairness-sensitive aggregation scheme then balances the participation through frequency compensation and damping on staleness. The results of the experiment indicate that SAFL achieves a 75% accuracy in 620 seconds, 27% higher than the state-of-the-art Federated Asynchronous Mobile Update (FedASMU) and increases the fairness index by 0.52 to 0.87. SAFL has a scalable, fair approach to the regulation of heterogeneous clusters, which means they can be used to ensure almost equal contribution in regulated settings such as financial and healthcare analytics.

DASFL: Dynamic Adaptive Split Federated Learning for Heterogeneous Clients

FedEdge-Adapt: Adaptive Federated Learning for Heterogeneous Edge AI Systems

Federated learning (FL) on heterogeneous edge networks faces a fundamental tension: standard aggregation protocols assume client homogeneity, yet real-world edge deployments span device tiers with $7 \times$ compute and $\mathbf{1 0} \times$ bandwidth disparities. Slow clients become stragglers that stall synchronous rounds, while uniform gradient compression degrades accuracy on bandwidth-constrained devices. This paper presents FedEdge-Adapt, a novel adaptive federated learning framework that jointly addresses device heterogeneity, straggler mitigation, and communication efficiency without sacrificing model quality. FedEdge-Adapt introduces three tightly coupled mechanisms: (1) tier-aware gradient compression that applies device-class-specific sparsification ratios, (2) drift-corrected aggregation that reweights client updates based on staleness and data heterogeneity, and (3) predictive client selection that anticipates dropout-prone devices using a lightweight resource oracle. We evaluate FedEdge-Adapt on a 30-node heterogeneous edge network over 150 communication rounds using the CIFAR-10 dataset under non-IID distributions ($\alpha=0.5$ Dirichlet) and compare against FedAvg, FedProx, and SCAFFOLD baselines. FedEdge-Adapt achieves 85.44% global accuracy, a 6.85 percentage-point improvement over FedAvg, while simultaneously reducing round latency by $\mathbf{6 7. 4 \%}$, communication overhead by $\mathbf{3 4. 0 \%}$, and client dropout rate by $\mathbf{5 4. 0 \%}$. Convergence is reached in 18 rounds versus 31 for FedAvg. Extended experiments across 100+ rounds confirm long-term stability with no late-stage divergence.

When Split Federated Learning Meets Prototype Learning: A Communication-Efficient Approach in Wireless Networks

Nowadays, split federated learning (SFL) has emerged as an effective paradigm for enabling privacy-preserving collaborative intelligence across heterogeneous devices with limited computation. However, SFL incurs significant communication overhead in wireless networks due to the uplink transmission of high-dimensional smashed data, which degrades network efficiency. To mitigate the communication bottleneck, we propose a prototype-based SFL framework ProtoSFL. Specifically, each selected client computes local prototypes for observed classes and uploads them to the server. Based on the received prototypes, the server derives global prototypes and optimizes a weighted objective that combines classification loss with prototype alignment loss. The server then updates the model accordingly and returns personalized prototype gradients to the clients. Simulation results verify the effectiveness of ProtoSFL in reducing communication overhead, achieving a substantial reduction in uplink communication, while maintaining competitive testing accuracy under various heterogeneous data settings compared with SFL baselines.

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.

Robust Federated Learning Under Real-World Client Churn

FeLiX is presented, an FL orchestration framework that minimizes wall-clock time-to-target accuracy on live interaction streams and achieves near-oracular performance in real-world settings.