FDA-Opt unifies and improves upon FedOpt and FDA for communication-efficient federated fine-tuning of language models on NLP tasks, outperforming optimized FedOpt baselines.
Communication-Efficient Learning of Deep Networks from Decen- tralized Data
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
Hierarchical federated learning for plant-disease classification shows distinct accuracy-versus-energy trade-offs across EfficientNet-B0, ResNet-50, and MobileNetV3-Large paired with FedAvg, FedProx, and FedAvgM.
citing papers explorer
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Communication-Efficient Federated Fine-Tuning
FDA-Opt unifies and improves upon FedOpt and FDA for communication-efficient federated fine-tuning of language models on NLP tasks, outperforming optimized FedOpt baselines.
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Understanding Communication Backends in Cross-Silo Federated Learning
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
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Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
Hierarchical federated learning for plant-disease classification shows distinct accuracy-versus-energy trade-offs across EfficientNet-B0, ResNet-50, and MobileNetV3-Large paired with FedAvg, FedProx, and FedAvgM.