Integrating Particle Swarm Dynamics into Federated Learning: A Promising Hybrid to Counter Client Drift under Non-IID Conditions

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Federated learning (FL) enables distributed model training without centralizing raw data, making it attractive for privacy-sensitive Internet of Things (IoT) environments.However, conventional FL algorithms suffer from slow convergence and degraded performance under statistically heterogeneous (non-IID) data distributions due to client drift.This paper proposes a Hybrid Federated-Swarm Optimization (HFSO) algorithm, which integrates particle swarm optimization (PSO) dynamics-velocity smoothing, personal-best memory, and global-best coordination-into the client update rule of stochastic gradient descent.The primary objective is to accelerate convergence and improve robustness under severe non-IID conditions without increasing per-round communication payload.Experimental evaluation is conducted using a unified PyTorch implementation on CIFAR-10 (α=0.1, 500 rounds, 10 clients/round) and FEMNIST for ablation and sensitivity studies.At round 500, the accuracy of HFSO on CIFAR-10 is 74.84% ± 0.62 while FedAvg is 62.44% ± 1.15 with the same settings.This final accuracy differs from but follows threshold crossing speed; while the time required for the initial threshold crossing varies between 180 ± 8 rounds on average across each of 5 independent runs (seeds {42, 123, 256, 512, 1024}), it is shown that the time when the threshold is crossed remains below 75% accuracy by round 500 as a result of non-IID-induced variance among clients.The pattern of transient-crossing is the same in FedAvg, reaching 75% accuracy at 320 ± 15 rounds on average, and then decreasing to the reported accuracy level within the budget of 500 rounds.Under the same CIFAR-10 conditions, HFSO outperforms recent heterogeneity-aware methods (FedNova, MOON, CCVR) within this specific experimental setup.Distributional analysis shows improved worst-client accuracy and reduced inter-client variance.An empirical privacy evaluation under a limited, honest-but-curious, white-box threat model shows reduced attack success rates relative to FedAvg, though no formal differential privacy guarantees are claimed.Additional memory overhead is approximately 3× (storing velocity and personal-best vectors), and computational overhead is 10-15% per local epoch.These results indicate that swarm-based coordination is a promising hybrid direction for improving convergence stability and communication efficiency in FL under non-IID conditions, within the specific experimental contexts evaluated (CIFAR-10/FEMNIST, α=0.1, 500 rounds).Generalization beyond these settings requires further validation.