constraint-aware-aggregation-for-federated-reinfor

name	constraint-aware-aggregation-for-federated-reinfor
description	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...
version	1.0.0
author	Usman Haider, Karl Mason
arxiv_id	2607.12763
created	2026-07-14T00:00:00.000Z
category	other
tags	["cs.LG","cs.AI"]
activation_keywords	["constraint","aware","aggregation","federated","reinforcement","learning","microgrid","energy","coordination","fedrl"]

Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination

Overview

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 provides the most reliable trade-off between reward and safety, without requiring dual optimization or modifications to local training. \textcolor{black}{We evaluate our approach on DairyGridEnv, a benchmark modeling multiple farms coordinating battery storage under stochastic demand and a shared grid capacity constraint, and further assess robustness using real load-driven demand profiles from Finland and the German FIELD dataset. Across multiple seeds, penalty-based aggregation substantially reduces violations while improving reward relative to FedAvg in both synthetic and real load-driven settings.} A combined reward-violation scheme exposes a tunable trade-off via $λ$, but is less stable. These results demonstrate that lightweight aggregation strategies can substantially improve empirical safety in federated reinforcement learning while preserving standard communication protocols.

Key Insights

• TODO: Extract key insights from the paper

Implementation Approach

• TODO: Describe how to implement the techniques from this paper

Applications

• TODO: List potential applications

Activation Keywords

constraint, aware, aggregation, federated, reinforcement, learning, microgrid, energy, coordination, fedrl

---