In　order　to　tackle　the　challenge　of　centralized　reinforcement　learning　based　microgrid　energy　management　would　impose　severe　privacy　violation　and　consume　a　large　number　of　communication　resource,　we　address　the　problem　of　microgrid　energy　management　strategy　(EMS)　based　on　edge-cloud　assisted　federated　deep　reinforcement　learning　(FDRL).　We　first　present　the　microgrid　operation　model　in　edge-cloud　computing　scenario　to　optimize　energy　management　strategy　with　economic　benefits　as　the　goal.　Then,　the　federated　dueling　deep　Q-network　with　novel　action　exploration　(DDQN)　is　proposed　to　resolve　the　problem　model　and　it　is　leveraging　to　design　an　innovative　microgrid　energy　management　strategy,　and　the　convergence　and　computational　complexity　of　the　proposed　energy　management　strategy　is　analyzed.　Simulation　results　validate　that　the　reward　and　profit　obtained　with　the　edge-cloud-assisted　federated　DDQN　energy　management　strategy　outperform　the　conventional　deep　reinforcement　learning　(DRL)　method,　and　the　communication　efficiency　and　data　privacy　preservation　can　be　attained.　©　2024　Elsevier　Ltd