Reliability-Aware Client Selection in Asynchronous and Heterogeneous Federated Learning

DASFL: Dynamic Adaptive Split Federated Learning for Heterogeneous Clients

URP-FL: Robust and Personalized Federated Learning under Heterogeneous and Adversarial Conditions

Federated learning is appealing for privacy-sensitive network systems, yet its practical deployment remains hindered by the following three recurring challenges: (1) client drift under non-IID data, (2) vulnerability to corrupted updates, and (3) the communication cost of repeated model exchange. Most existing approaches address these issues in isolation. While analytically convenient, this separation often fails to reflect real-world conditions. For instance, defenses against poisoning may suppress useful updates, while personalization and compression can alter the aggregation geometry itself. In this paper, we study these effects jointly and propose URP-FL, a compact training framework that integrates reliability-aware aggregation, local regularization for drift control, and sparse client uploads. We provide theoretical analysis establishing a convergence bound with distinct terms capturing optimization error, data heterogeneity, and adversarial impact. Experiments on a non-IID image classification benchmark with sign-flip and label-flip attacks demonstrate the benefits of the unified design. Compared to FedAvg and FedProx, this URP-FL maintains accuracy under attack while reducing transmitted parameters by approximately 75%. Rather than presenting a production ready system, it offers a reproducible and technically coherent step toward federated learning that is more robust under realistic conditions.

Trust-Aware Topology Learning for Dynamic Decentralized Federated Learning under Adversaries

In dynamic mobile decentralized federated learning (DFL), adversaries can poison both model updates and the topology information devices use to choose collaborators. We present DMTT (Dynamic MURMURA with Trusted Topology), a decentralized personalized FL protocol built on MURMURA, which uses evidential deep learning to down-weight distribution-mismatched peers, extended here to time-varying graphs under topology-manipulation attacks. Each device maintains a confidence-weighted local topology view from link-reliability estimates, signed topology claims, witness corroboration, and a Beta-distributed source-trust model, then aggregates only over a trust-screened collaborator set using a composite score fusing model compatibility, topology trust, and link reliability. We prove the screened mixing matrices confine Byzantine influence to a bounded residual $\delta_{max}$ that vanishes under perfect screening, and implement DMTT as a coordinator-free distributed system with each client running as an independent ZeroMQ process synchronized by a shared wall-clock epoch. On UCI HAR and PAMAP2, each partitioned across 100 mobile clients with Dirichlet heterogeneity, DMTT sustains honest-node accuracy above 0.862 (UCI HAR) and 0.829 (PAMAP2) across all tested adversary fractions (10 to 80%), nearly matching no-attack accuracy at low fractions and degrading gracefully toward local-only performance at extremes; static and dynamic FedAvg collapse to chance at every fraction, and robust aggregators (Krum, BALANCE, UBAR) fail to consistently beat a local-only baseline, while DMTT is the only method that clears this bar across both datasets at all fractions, with surviving Byzantine aggregation weight empirically zero throughout, consistent with $\delta_{max}$=0. The protocol runs end-to-end on real nodes via a coordinator-free ZeroMQ backend on the Melbourne Research Cloud.

Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning

The Krum-Proxy attack is introduced, a selection-aware backdoor injection strategy that consistently bypasses Byzantine-robust aggregation and constructs adversarial updates that are not only similar to benign updates but are also optimized to lie in regions of the update space that are favored during aggregation.

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.

Aggregate-Only Observability for Privacy–Utility Tradeoffs in Federated Learning

Federated Learning (FL) avoids centralizing raw data, but server-side access to per-client updates still creates a significant privacy risk because gradients can leak sensitive information through inversion and related attacks. A common defense is client-level Differential Privacy (DP), which reduces attack fidelity by perturbing individual updates, but often at the cost of degraded predictive utility, especially in heterogeneous settings. In this paper, we study a different design choice: aggregate-only observability, where the server learns only an aggregate update rather than the individual client contributions that compose it. We develop a unified evaluation framework comparing baseline FL, client-level DP-FL, an idealized sum-only aggregation mechanism (hide_sum), and a quantum-inspired aggregate-observation primitive (qsum) based on finite-shot GHZ-style phase estimation. Experiments on Texas PUDF and MIMIC-IV show that the aggregate-only regimes preserve competitive predictive utility relative to baseline FL while removing the direct per-client observation channel used by standard server-side inversion attacks. We also analyze utility sensitivity to shot budgets and the resource scaling of the quantum-inspired primitive. The results suggest that privacy–utility tradeoffs in FL depend not only on how updates are perturbed, but also on what the server is allowed to observe. Aggregate-only observability therefore provides a useful mechanism-level complement to noise-based privacy defenses, while also motivating hybrid designs to address residual leakage from aggregate updates.