Exploration Across Small Silos: Federated Few-Shot Learning on Network Edge

Zhao, Cong; Sun, Xin-Yue; Yang, Shusen; Ren, Xuebin; Zhao, Peng; McCann, Julie A.

doi:10.1109/mnet.111.2100329

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Some preliminary work that combines FSL with FL has been done. For example, Zhao et al [272] propose a MAML based federated FSL method for supervised classification, but they focus on a cross-silo FL setting, where the data is siloed across a small number of participants so that the participants have mutually isolated classes of samples. In cross-silo FL [143], the participants are usually institutions or organizations, and they are assumed to be highly available during training.…”

Section: B Decision Making and Negotiationmentioning

confidence: 99%

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Kokkonen¹,

Lovén²,

Motlagh³

et al. 2022

Preprint

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Future AI applications require performance, reliability, and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.

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Section: B Decision Making and Negotiationmentioning

confidence: 99%

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Kokkonen¹,

Lovén²,

Motlagh³

et al. 2022

Preprint

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show abstract

“…Moreover, with the rising privacy consciousness of people and the improvement of data security law, it is difficult for the power grid company to collect and analyze the power load data from clients [9]. Federated learning (FL) [10][11][12][13], proposed by Google, can effectively protect the raw power load data privacy of all clients. Concretely, in the federated settings, the server coordinates massive clients to train a shared machinelearning model by frequently transmitting model pa- rameters instead of collecting the raw power load data on each client.…”

Section: Introductionmentioning

confidence: 99%

Communication-Efficient Federated Learning for Power Load Forecasting in Electric IoTs

Mao

Huang

et al. 2023

IEEE Access

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With the construction of the modern power system, power load forecasting is significant to keep the electric Internet of Things in operation. However, it usually needs to collect massive power load data on the server and may face the problem of privacy leakage of raw data. Federated learning can enhance the privacy of the raw power load data of clients by frequently transmitting model updates. Concerning the increasing communication burden of resource-heterogeneous clients resulting from frequent communication with the server, a communication-efficient federated learning algorithm based on Compressed Model Updates and Lazy uploAd (CMULA-FL) was proposed to reduce the communication cost. CMULA-FL also integrates the error compensation strategy to improve the model utility. First, the compression operator is used to compress the transmitted model updates, of which large norms are uploaded to reduce the communication cost of each epoch and transmission frequency. Second, by measuring the error of compression and lazy upload, the error is accumulated to the next epoch to improve the model utility. Finally, based on simulation experiments on the benchmark power load data, the results show that the communication cost decreases at least 60% with controlled loss of model prediction compared with baseline. INDEX TERMS Power load forecasting, federated learning, quantization, lazy upload, error compensation.

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FedFM: A federated few-shot learning method by comparison network and model calibration

Zhao,

Bao,

Chen

et al. 2025

Knowledge-Based Systems

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Exploration Across Small Silos: Federated Few-Shot Learning on Network Edge

Cited by 4 publications

References 8 publications

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Communication-Efficient Federated Learning for Power Load Forecasting in Electric IoTs

FedFM: A federated few-shot learning method by comparison network and model calibration

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