Personalized　Federated　Learning　(PFL)　aims　to　train　machine　learning　models　on　decentralized,　heterogeneous　data　while　preserving　user　privacy.　This　research　survey　examines　the　core　communication　challenges　in　PFL　and　evaluates　optimization　strategies　to　address　key　issues,　including　data　heterogeneity,　high　communication　costs,　model　drift,　privacy　vulnerabilities,　and　device　variability.　We　provide　a　comprehensive　analysis　of　key　communication　optimization　techniques;　Model　Compression,　Differential　Privacy,　Client　Selection,　Asynchronous　Updates,　Gradient　Compression,　and　Model　Caching,　by　their　efficiency　and　effectiveness　under　diverse　PFL　conditions.　Our　study　quantitatively　compares　these　methods,　identifies　limitations,　and　proposes　enhanced　strategies　to　improve　communication　efficiency,　reduce　latency,　and　maintain　model　accuracy.　This　research　delivers　actionable　insights　for　optimizing　PFL　communication,　enhancing　both　model　performance　and　privacy　safeguards.　Overall,　this　work　serves　as　a　valuable　resource　for　researchers　and　practitioners,　offering　practical　guidance　on　leveraging　advanced　communication　techniques　to　drive　PFL　improvements　and　highlighting　promising　directions　for　future　research.