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Dec 22, 2023 · We propose a data-free FL framework based on local-to-central collaborative distillation with direct input and output space exploitation.
We propose a data-free FL framework based on local-to-central collaborative distillation with direct input and output space exploitation.
Dec 22, 2023 · In this paper, we propose a new federated learning framework (FedIOD) that conducts a collaborative knowledge distillation in both the input and ...
Mar 28, 2024 · Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without ...
This work proposes a data-free FL framework based on local-to-central collaborative distillation with direct input and output space exploitation that ...
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Abstract—Federated learning enables the creation of a central- ized global model by aggregating updates from the locally trained.
Collaborative Distillation is a new knowledge distillation method (named Collaborative Distillation) for encoder-decoder based neural style transfer.
In FedAvg, collaborative learning proceeds in synchronous rounds by leveraging a client-server paradigm. Participants. (i.e., clients) iteratively exchange ...
Federated learning revolutionizes collaborative model training across decentralized edge devices, ensuring privacy by avoiding direct data sharing.