AdaptiveFedLoRA: Drift-Aware Adaptive LoRA Rank Scheduling for Federated Medical Small Language Models

Federated learning (FL) for medical small language models (SLMs) faces significant challenges due to client drift caused by non-IID clinical data distributions and heterogeneous hardware capabilities. Existing approaches, such as time-based LoRA rank scheduling, fail to adapt to dynamic drift patterns. We propose AdaptiveFedLoRA, a novel drift-aware adaptive LoRA rank scheduling framework that dynamically adjusts model capacity (LoRA rank) based on multi-faceted drift measurements. Unlike prior work that constrains updates (e.g., SCAFFOLD, FedProx), AO_SCPLOWDAPTIVEC_SCPLOWFO_SCPLOWEDC_SCPLOWLO_SCPLOWOC_SCPLOWRA dynamically allocates model capacity (via LoRA rank) in response to measured drift. Our approach combines (1) multi-faceted drift measurement (model, performance, and semantic drift), (2) adaptive rank scheduling that responds to drift levels, (3) intelligent client selection, and (4) specialty-aware aggregation using Jensen-Shannon divergence. We validate our method on simulated medical data with Qwen3-0.6B (0.6B parameters) across heterogeneous devices. Eexperimental results demonstrate improved convergence and substantially reduced drift relative to strong FL baselines (FedAvg, FedProx, FedNova, SCAFFOLD, SA-FedLoRA), while maintaining communication efficiency through adaptive parameterization. AO_SCPLOWDAPTIVEC_SCPLOWFO_SCPLOWEDC_SCPLOWLO_SCPLOWOC_SCPLOWRA achieves best performance across all tested scales (2-5 clients per round), achieving mean final loss of 0.4982-0.5841 across 2-5 client scales, with 15.5-52.4% improvement over strongest baselines, making it ideal for resource-constrained medical FL deployments. Comprehensive scale experiments (2, 3, and 5 clients per round) reveal scale-dependent performance patterns, with AO_SCPLOWDAPTIVEC_SCPLOWFO_SCPLOWEDC_SCPLOWLO_SCPLOWOC_SCPLOWRA maintaining consistent superiority and low variance across all scales, highlighting the importance of scale-dependent method selection. Downstream task evaluation on ICD-10-CM code prediction demonstrates that all methods achieve recall > 0.40 on zero-shot evaluation, confirming that federated learning preserves clinically useful representations that transfer to clinical tasks.