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Federated Meta-Learning with Fast Convergence and Efficient Communication

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arxiv 1802.07876 v2 pith:Q45IR2M5 submitted 2018-02-22 cs.LG cs.IR

Fei Chen , Mi Luo , Zhenhua Dong , Zhenguo Li , Xiuqiang He This is my paper · ORCID
classification cs.LGcs.IR
keywords federatedalgorithmfedmetalearningmeta-learningcommunicationconvergencedevices
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Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta-learning is a natural choice to handle these issues, and propose a federated meta-learning framework FedMeta, where a parameterized algorithm (or meta-learner) is shared, instead of a global model in previous approaches. We conduct an extensive empirical evaluation on LEAF datasets and a real-world production dataset, and demonstrate that FedMeta achieves a reduction in required communication cost by 2.82-4.33 times with faster convergence, and an increase in accuracy by 3.23%-14.84% as compared to Federated Averaging (FedAvg) which is a leading optimization algorithm in federated learning. Moreover, FedMeta preserves user privacy since only the parameterized algorithm is transmitted between mobile devices and central servers, and no raw data is collected onto the servers.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields

    cs.LG 2025-08 conditional novelty 7.0 of 10

    MDIR detects LLM weight homology from embedding matrices alone using polar decomposition and permutation matching, achieving perfect AUC and accuracy on LeaFBench and reconstructing layer-level transformations.

  2. On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations

    cs.LG 2024-11 conditional novelty 7.0 of 10

    The paper proves that personalized federated temporal-difference learning with a shared linear representation converges at rate O(1/(N^{2/3} T^{2/3})), yielding linear speedup in the number of agents under Markovian noise.

  3. Federated Learning with Heterogeneous and Private Label Sets

    cs.LG 2025-08 conditional novelty 6.0 of 10

    By averaging classifier weights per label across clients and tuning the central model on unlabeled data, federated learning can handle private, heterogeneous client label sets at accuracy close to the public-label setting.

  4. Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems

    cs.LG 2025-01 conditional novelty 5.0 of 10

    A VoL/TLW-based PDQN scheduler for NOMA federated meta-learning outperforms DDPG, OMA, equal-weight, and random baselines in simulation.

  5. Joint Channel Estimation and Dynamics-Aware Grouping for Time-Varying RIS-Assisted OTA Federated Learning

    cs.IT 2026-07 conditional novelty 4.0 of 10

    A GRU-based channel predictor plus mobility-aware user grouping reduces CSI and over-the-air aggregation error in RIS-assisted federated learning under imperfect, time-varying channels.

  6. Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shift

    cs.LG 2024-12 reject novelty 4.0 of 10

    FEDMPR, combining magnitude pruning, dropout, and noise injection in local training, reports accuracy gains over standard federated baselines on several image benchmarks, though not consistently in all settings.

  7. FedAH: Aggregated Head for Personalized Federated Learning

    cs.LG 2024-12 conditional novelty 4.0 of 10

    FedAH improves personalized federated learning by element-wise mixing each client's local head with the global head before local training, and it reports better accuracy than ten federated baselines on five benchmarks.

  8. FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction

    cs.AI 2024-12 conditional novelty 4.0 of 10

    A server-side personalized aggregation method for federated learning reduces 10-second vehicle speed prediction error by 0.8% over eleven baselines on a simulated urban driving dataset.

  9. On Gossip-based Information Dissemination in Pervasive Recommender Systems

    cs.SI 2019-08 conditional novelty 4.0 of 10

    A proximity-based gossip recommender design is introduced; the Android prototype can exchange ratings within about 6 meters, but the filtering and recommendation steps are not implemented.

  10. Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs

    cs.LG 2019-08 conditional novelty 3.0 of 10

    Adding MMD regularization or feature fusion modules to on-device training can reduce federated learning communication rounds by 20-60 percent on MNIST and CIFAR-10, according to the authors' experiments.

  11. Federated Learning: Challenges, Methods, and Future Directions

    cs.LG 2019-08 unverdicted

    This survey maps federated learning's core challenges, reviews existing methods, and lists open problems.

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