Collaborative Model Visual
%20aims%20to%20estimate%20the%20location%20of%20an%20image%20by%20treating%20it%20as%20a%20retrieval%20problem.%20VPR%20uses%20a%20database%20of%20geo-tagged%20images%20and%20leverages%20deep%20neural%20networks%20to%20extract%20a%20global%20representation%2C%20called%20descriptor%2C%20from%20each%20image.%20While%20the%20training%20data%20for%20VPR%20models%20often%20originates%20from%20diverse%2C%20geographically%20scattered%20sources%20(geo-tagged%20images)%2C%20the%20training%20process%20itself%20is%20typically%20assumed%20to%20be%20centralized.%20This%20research%20revisits%20the%20task%20of%20VPR%20through%20the%20lens%20of%20Federated%20Learning%20(FL)%2C%20addressing%20several%20key%20challenges%20associated%20with%20this%20adaptation.%20VPR%20data%20inherently%20lacks%20well-defined%20classes%2C%20and%20models%20are%20typically%20trained%20using%20contrastive%20learning%2C%20which%20necessitates%20a%20data%20mining%20step%20on%20a%20centralized%20database.%20Additionally%2C%20client%20devices%20in%20federated%20systems%20can%20be%20highly%20heterogeneous%20in%20terms%20of%20their%20processing%20capabilities.%20The%20proposed%20FedVPR%20framework%20not%20only%20presents%20a%20novel%20approach%20for%20VPR%20but%20also%20introduces%20a%20new%2C%20challenging%2C%20and%20realistic%20task%20for%20FL%20research%2C%20paving%20the%20way%20to%20other%20image%20retrieval%20tasks%20in%20FL.)
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