Through　deploying　computing　resources　at　the　network　edge,　Mobile　Edge　Computing　(MEC)　alleviates　the　contradiction　between　the　high　requirements　of　intelligent　mobile　applications　and　the　limited　capacities　of　mobile　End　Devices　(EDs)　in　smart　communities.　However,　existing　solutions　of　computation　offloading　and　resource　allocation　commonly　rely　on　prior　knowledge　or　centralized　decision-making,　which　cannot　adapt　to　dynamic　MEC　environments　with　changeable　system　states　and　personalized　user　demands,　resulting　in　degraded　Quality-of-Service　(QoS)　and　excessive　system　overheads.　To　address　this　important　challenge,　we　propose　a　novel　Personalized　Federated　deep　Reinforcement　learning　based　computation　Offloading　and　resource　Allocation　method　(PFR-OA).　This　innovative　PFR-OA　considers　the　personalized　demands　in　smart　communities　when　generating　proper　policies　of　computation　offloading　and　resource　allocation.　To　relieve　the　negative　impact　of　local　updates　on　global　model　convergence,　we　design　a　new　proximal　term　to　improve　the　manner　of　only　optimizing　local　Q-value　loss　functions　in　classic　reinforcement　learning.　Moreover,　we　develop　a　new　partial-greedy　based　participant　selection　mechanism　to　reduce　the　complexity　of　federated　aggregation　while　endowing　sufficient　exploration.　Using　real-world　system　settings　and　testbed,　extensive　experiments　demonstrate　the　effectiveness　of　the　PFR-OA.　Compared　to　benchmark　methods,　the　PFR-OA　achieves　better　trade-offs　between　delay　and　energy　consumption　and　higher　task　execution　success　rates　under　different　scenarios.　IEEE