Deploying machine learning in distributed edge environments where participants are economic competitors presents fundamental challenges unaddressed by traditional MLOps frameworks. Existing federated learning assumes collaborative participants with reliable connectivity, while differential privacy applies uniform protection regardless of competitive relationships. In agricultural deployments, farms growing identical crops compete in commodity markets yet benefit from shared model training—requiring privacy guarantees that prevent competitive intelligence leakage without destroying model utility. We present an MLOps architecture for adversarial edge networks, contributing: (1) adaptive privacy budgets that allocate differential privacy protection based on competitive overlap between participants, (2) time-window aggregation with convergence under multi-day network partitions, and (3) edge-based data quality validation that identifies corrupted sensor readings before distributed training. Production deployment across agricultural edge networks demonstrates that adaptive privacy demonstrates a tunable trade-off, achieving 84.2% accuracy in high-competition scenarios (preventing 77% of attacks) compared to 97.8% in low-competition settings. Time-window aggregation enables graceful degradation of model utility during multi-day partitions, maintaining a stable but reduced performance profile.

@inproceedings{paperbot11658,
  title = {Engineering MLOps for Adversarial Edge Networks in Agricultural Deployments},
  author = {Vasu Jindal},
  booktitle = {ICSE},
  year = {2026},
  doi = {10.1145/3774748.3787759}
}