When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity

arXiv:2605.08992 · cs.LG · Submitted 2026-05-09 · Read on arXiv

cs.LG

Submitted: 2026-05-09

Updated: 2026-09-20

Comments: 7 pages, 5 figures. Accepted at FL@FM-IJCAI 2026 Workshop, Bremen, Germany

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 51× in this paper · explained in 1 episode
• Foundation Model — 27× in this paper · explained in 3 episodes
• Aggregation — 23× in this paper · explained in 1 episode
• Foundation Models — 18× in this paper · explained in 7 episodes
• Federated Learning — 15× in this paper · explained in 14 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Non-IID Data — 4× in this paper · explained in 2 episodes
• Foundation Models (FMs) — 3× in this paper · explained in 2 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Data Heterogeneity — 2× in this paper · explained in 3 episodes
• Federated Learning (FL) — 2× in this paper · explained in 7 episodes
• In International Conference on Learning Representations — 2× in this paper
• LoRA Adapters — 2× in this paper · explained in 3 episodes
• alpha — 2× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 1× in this paper
• Meta-learning — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• Federated Learning for Mobile Keyboard Prediction
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
• Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
• PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic Segmentation
• Double trouble: Predicting new variant counts across two heterogeneous populations

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