REVIEW 4 cited by
Knowledge Distillation in Federated Edge Learning: A Survey
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The increasing demand for intelligent services and privacy protection of mobile and Internet of Things (IoT) devices motivates the wide application of Federated Edge Learning (FEL), in which devices collaboratively train on-device Machine Learning (ML) models without sharing their private data. Limited by device hardware, diverse user behaviors and network infrastructure, the algorithm design of FEL faces challenges related to resources, personalization and network environments. Fortunately, Knowledge Distillation (KD) has been leveraged as an important technique to tackle the above challenges in FEL. In this paper, we investigate the works that KD applies to FEL, discuss the limitations and open problems of existing KD-based FEL approaches, and provide guidance for their real deployment.
Forward citations
Cited by 4 Pith papers
-
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
A unified benchmark of 18 federated learning methods under synthetic, human-annotation, and systematic label noise finds no consistently winning method and shows that an SVD-based decorrelation regularizer improves mo...
-
SVAFD: A Secure and Verifiable Co-Aggregation Protocol for Federated Distillation
SVAFD signs per-slice sums of logits, not the logits themselves, so a malicious server can change the teacher knowledge without breaking the proof.
-
A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives
The paper classifies model extraction attacks and defenses into attack, defense, and computing environment categories and surveys their current state.
-
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
FedEEC trains larger cloud models in end-edge-cloud federated learning via bridge-sample distillation and self-rectified knowledge transfer, reporting accuracy gains on SVHN, CIFAR-10, and CINIC-10.
Discussion (0). Continue with ORCID to comment.