From Task Mixtures to Specialized Experts
In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data distributions, or tasks. Conventional federated learning primarily addresses heterogeneity across clients without explicitly resolving latent task mixtures within each client. We study this setting as compound heterogeneity, where data is heterogeneous both across and within clients. We study adaptation over a common frozen representation and show that, when tasks share the same feature geometry, the optimal model for a client's task mixture under squared loss is a convex combination of the optimal models for its underlying tasks. Thus, a s...
| Authors | Hojat Allah Salehi, Mehrdad Mahdavi, Andrew Arash Mahyari et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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