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This paper introduces OCO-PAoI-Hard, an online convex optimization framework for multi-sensor IoT scheduling that enforces hard per-slot peak Age-of-Information deadlines under adversarial channels, achieving zero modeled-state deadline violations and O(√T) regret.
This paper proposes the first FTRL-type algorithms for decentralized online convex optimization with compressed communication, achieving elegant theoretical guarantees and improved regret bounds compared to previous OGD-type methods.