Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination
Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior. In this work, we study constraint-aware aggregation for federated reinforcement learning in distributed energy coordination. We propose aggregation rules that incorporate both local performance and estimated constraint violation into the server-side update. Among these, a simple penalty-based rule, $w_i \propto R_i - αV_i$, consistently p
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- PossiblePossibly related (embedding) · 48%jaimasih05-commits/swarm-foraging-qlearn →
- LinkedLinked via arxiv author · 85%Usman Haider →
“Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination”
- LinkedLinked via arxiv author · 85%Karl Mason →
“Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Constraint-Aware Aggregation for Federated Reinforcement Lea” ≈ “aymericdamien/TopDeepLearning””