Brain　tumors,　characterized　by　uncontrollable　cellular　growths,　are　a　significant　global　health　challenge.　Navigating　the　complexities　of　tumor　identification　due　to　their　varied　dimensions　and　positions,　our　research　introduces　enhanced　methods　for　precise　detection.　Utilizing　advanced　learning　techniques,　we＇ve　improved　early　identification　by　preprocessing　clinical　dataset-derived　images,　augmenting　them　via　a　Generative　Adversarial　Network,　and　applying　an　Improved　Privacy-Preserving　Collaborative　Convolutional　Neural　Network　(IPC-CNN)　for　segmentation.　Recognizing　the　critical　importance　of　data　security　in　today＇s　digital　era,　our　framework　emphasizes　the　preservation　of　patient　privacy.　We　evaluated　the　performance　of　our　proposed　model　on　the　Figshare　and　BRATS　2018　datasets.　By　facilitating　a　collaborative　model　training　environment　across　multiple　healthcare　institutions,　we　harness　the　power　of　distributed　computing　to　securely　aggregate　model　updates,　ensuring　individual　data　protection　while　leveraging　collective　expertise.　Our　IPC-CNN　model　achieved　an　accuracy　of　99.40%,　marking　a　notable　advancement　in　brain　tumor　classification　and　offering　invaluable　insights　for　both　the　medical　imaging　and　machine　learning　communities.