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Found 4 papers, 2 papers with code
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs.
Fairness-Aware Client Selection for Federated Learning
By not using threshold-based reputation filtering, it provides FL clients with opportunities to redeem their reputations after a perceived poor performance, thereby further enhancing fair client treatment.
FedGH: Heterogeneous Federated Learning with Generalized Global Header
It is a communication and computation-efficient model-heterogeneous FL framework which trains a shared generalized global prediction header with representations extracted by heterogeneous extractors for clients' models at the FL server.
Towards AI-Empowered Crowdsourcing
Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e. g., Uber, Airbnb).
