{"ID":6138323,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T15:23:10.408442879Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07529","arxiv_id":"2607.07529","title":"FedMark-FM: Auditable, Risk-Adjusted Data Markets for Federated Foundation-Model Adaptation","abstract":"Federated foundation-model adaptation increasingly relies on heterogeneous private artifacts (retrieval corpora, prompts and demonstrations, LoRA adapters, preference and safety data, and update sketches), yet existing federated-learning incentive mechanisms price clients as homogeneous data or update providers. This assumption poorly matches foundation-model pipelines, where contribution value is heterogeneous, non-IID, pipeline-dependent, privacy-constrained, and vulnerable to strategic behavior. We propose FedMark-FM, an auditable, risk-adjusted data-market framework that models clients as sellers of typed artifacts, estimates marginal contribution with S3Val, a stratified, uncertainty-aware Shapley estimator supporting pipeline-ordered valuation, and converts lower-confidence-bound values into budget-feasible payments penalizing duplication, sybil splitting, poisoned adapters, privacy-budget gaming, and cost inflation. We evaluate FedMark-FM-Bench across FEVER retrieval, held-out generator-backed RAG, and trained PEFT/LoRA tracks. Under a held-out prompt-injection poisoner, FedMark-FM improves downstream accuracy by 7.5-8.1 points over volume, leave-one-out, and FL-Shapley while selecting zero strategic clients. Split-conformal calibration reaches full lower-bound coverage at mean width 0.0141, versus 0.33 for naive intervals. We prove pipeline-ordered valuation is the unique credit rule respecting serving causality, and show it materially changes credit assignment (Spearman 0.76, selected-set overlap 0.67) while leaving held-out task quality unchanged; the market preserves rare specialists with audit-ready ledgers at 200-1000-client scale. FedMark-FM shows incentives for federated foundation models can be engineered as auditable data infrastructure coupling valuation, mechanism design, privacy interfaces, and pipeline-order semantics.","short_abstract":"Federated foundation-model adaptation increasingly relies on heterogeneous private artifacts (retrieval corpora, prompts and demonstrations, LoRA adapters, preference and safety data, and update sketches), yet existing federated-learning incentive mechanisms price clients as homogeneous data or update providers. This a...","url_abs":"https://arxiv.org/abs/2607.07529","url_pdf":"https://arxiv.org/pdf/2607.07529v1","authors":"[\"Phat T. Tran-Truong\",\"Xuan-Bach Le\",\"Minh Nhat Nguyen\"]","published":"2026-07-08T15:23:30Z","proceeding":"cs.GT","tasks":"[\"cs.GT\"]","methods":"[\"LoRA\"]","has_code":false}