Federated Learning Gradient Generator
%20has%20emerged%20as%20a%20machine%20learning%20approach%20able%20to%20preserve%20the%20privacy%20of%20user's%20data.%20Applying%20FL%2C%20clients%20train%20machine%20learning%20models%20on%20a%20local%20dataset%20and%20a%20central%20server%20aggregates%20the%20learned%20parameters%20coming%20from%20the%20clients%2C%20training%20a%20global%20machine%20learning%20model%20without%20sharing%20user's%20data.%20However%2C%20the%20state-of-the-art%20shows%20several%20approaches%20to%20promote%20attacks%20on%20FL%20systems.%20For%20instance%2C%20inverting%20or%20leaking%20gradient%20attacks%20can%20find%2C%20with%20high%20precision%2C%20the%20local%20dataset%20used%20during%20the%20training%20phase%20of%20the%20FL.%20This%20paper%20presents%20an%20approach%2C%20called%20Deep%20Leakage%20from%20Gradients%20with%20Feedback%20Blending%20(DLG-FB)%2C%20which%20is%20able%20to%20improve%20the%20inverting%20gradient%20attack%2C%20considering%20the%20spatial%20correlation%20that%20typically%20exists%20in%20batches%20of%20images.%20The%20performed%20evaluation%20shows%20an%20improvement%20of%2019.18%25%20and%2048%2C82%25%20in%20terms%20of%20attack%20success%20rate%20and%20the%20number%20of%20iterations%20per%20attacked%20image%2C%20respectively.)
%20schemes%20allow%20multiple%20participants%20to%20collaboratively%20train%20neural%20networks%20without%20the%20need%20to%20directly%20share%20the%20underlying%20data.However%2C%20in%20early%20schemes%2C%20all%20participants%20eventually%20obtain%20the%20same%20model.%20Moreover%2C%20the%20aggregation%20is%20typically%20carried%20out%20by%20a%20third%20party%2C%20who%20obtains%20combined%20gradients%20or%20weights%2C%20which%20may%20reveal%20the%20model.%20These%20downsides%20underscore%20the%20demand%20for%20fair%20and%20privacy-preserving%20FL%20schemes.%20Here%2C%20collaborative%20fairness%20asks%20for%20individual%20model%20quality%20depending%20on%20the%20individual%20data%20contribution.%20Privacy%20is%20demanded%20with%20respect%20to%20any%20kind%20of%20data%20outsourced%20to%20the%20third%20party.%20Now%2C%20there%20already%20exist%20some%20approaches%20aiming%20for%20either%20fair%20or%20privacy-preserving%20FL%20and%20a%20few%20works%20even%20address%20both%20features.%20In%20our%20paper%2C%20we%20build%20upon%20these%20seminal%20works%20and%20present%20a%20novel%2C%20fair%20and%20privacy-preserving%20FL%20scheme.%20Our%20approach%2C%20which%20mainly%20relies%20on%20homomorphic%20encryption%2C%20stands%20out%20for%20exclusively%20using%20local%20gradients.%20This%20increases%20the%20usability%20in%20comparison%20to%20state-of-the-art%20approaches%20and%20thereby%20opens%20the%20door%20to%20applications%20in%20control.)
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