US EditionHtFLlib: A Unified Benchmarking Library for Evaluating Heterogeneous Federated Learning Methods Across Modalities – #CryptoUpdatesGNIT
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HtFLlib: A Unified Benchmarking Library for Evaluating Heterogeneous Federated Learning Methods Across Modalities – #CryptoUpdatesGNIT
AI institutions develop heterogeneous models for specific tasks but face data scarcity challenges during training. Traditional Federated Learning (FL) supports only homogeneous model collaboration, which needs identical architectures across all clients. However, clients develop model architectures for their unique requirements. Moreover, sharing effort-intensive locally trained models contains intellectual property and reduces participants’ inte…
HtFLlib: A Unified Benchmarking Library for Evaluating Heterogeneous Federated Learning Methods Across Modalities
AI institutions develop heterogeneous models for specific tasks but face data scarcity challenges during training. Traditional Federated Learning (FL) supports only homogeneous model collaboration, which needs identical architectures across all clients. However, clients develop model architectures for their unique requirements. Moreover, sharing effort-intensive locally trained models contains intellectual property and reduces participants’ inte…
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