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Federated Mutual Learning
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Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about distinctive challenges to the canonical federated learning algorithm (FedAvg). First, due to the Non-IIDness of data, the global shared model may perform worse than local models that solely trained on their private data; Second, the objective of center server and clients may be different, where center server seeks for a generalized model whereas client pursue a personalized model, and clients may run different tasks; Third, clients may need to design their customized model for various scenes and tasks; In this work, we present a novel federated learning paradigm, named Federated Mutual Leaning (FML), dealing with the three heterogeneities. FML allows clients training a generalized model collaboratively and a personalized model independently, and designing their private customized models. Thus, the Non-IIDness of data is no longer a bug but a feature that clients can be personally served better. The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.
Forward citations
Cited by 6 Pith papers
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AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning
AS-FedBridge trains a shared pseudo-spike bridge so ANN and SNN clients in federated learning can align representations and improve collaborative accuracy.
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FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning
Sharing class-relation topology with reliability weighting beats parameter, distillation, and prototype sharing under heterogeneous federated backbones on CIFAR and Tiny-ImageNet.
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H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
H2Tune enables federated fine-tuning across heterogeneous foundation models by sharing sparsified rank-aligned middle matrices with learned layer mappings and alternating shared/private updates.
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HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark
HtFLlib is a unified benchmark and library with 12 datasets, 40 heterogeneous model architectures, and systematic accuracy, convergence, and cost evaluations of 10 HtFL methods.
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TinyProto: Communication-Efficient Federated Learning with Sparse Prototypes in Resource-Constrained Environments
Compressing class prototypes with per-class masks and a sample-count scaling trick cuts communication cost in prototype-based federated learning by up to several times without hurting accuracy.
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Heterogeneous Federated Learning with Prototype Alignment and Upscaling
ProtoNorm adds server-side prototype alignment and a per-dataset scaling factor to FedProto-style federated learning, improving accuracy but with the gain largely driven by the tuned scaling factor.
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