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Xianda Wang

2 accepted papers

2026

FedBRICK: Structural Bias Aware Heterogeneous Foundation Model Federated Tuning

AAAI 2026technical

Model-heterogeneous federated tuning (MHFT) enables the privacy-preserving fine-tuning of foundation models in heterogeneous systems by allowing clients and the server to adopt different model architectures. Depth partial training—where each client updates only a subset of the model

Cited by 0SourcePDFScholar
2025

Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning

AAAI 2025technical

In recent years, the distributed training of foundation models (FMs) has seen a surge in popularity. In particular, federated learning enables collaborative model training among edge clients while safeguarding the privacy of their data. However, federated training of FMs across resource-constrained…

Cited by 0SourcePDFScholar