REVIEW 6 cited by
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
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
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
read the original abstract
Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue, we introduce two novel strategies to reduce communication costs: (1) the use of lossy compression on the global model sent server-to-client; and (2) Federated Dropout, which allows users to efficiently train locally on smaller subsets of the global model and also provides a reduction in both client-to-server communication and local computation. We empirically show that these strategies, combined with existing compression approaches for client-to-server communication, collectively provide up to a $14\times$ reduction in server-to-client communication, a $1.7\times$ reduction in local computation, and a $28\times$ reduction in upload communication, all without degrading the quality of the final model. We thus comprehensively reduce FL's impact on client device resources, allowing higher capacity models to be trained, and a more diverse set of users to be reached.
Forward citations
Cited by 6 Pith papers
-
Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven
Two randomized Hadamard transforms suffice to make coordinate marginals O(d^{-1/2})-close to Gaussian for most quantization methods, with three needed for vector quantization to match uniform random rotations asymptotically.
-
Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
A device-partitioning bandwidth allocation policy for federated learning over IIoT networks that provably reduces total training time compared to any non-partitioning scheme.
-
DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
DFedReweighting is a unified reweighting method for decentralized federated learning that customizes aggregation via target metrics and strategies to improve fairness, Byzantine robustness, and other objectives while ...
-
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
HASA computes client heterogeneity scores from local data and assigns wider subnets to less heterogeneous clients, raising mean client test accuracy from 13.82% to 14.32% and improving worst-client accuracy versus uni...
-
Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy
Adaptive bit-length schedulers plus Laplacian DP in non-IID FL reduce communicated data by up to 52.64% on MNIST and 45% on CIFAR-10 while keeping competitive accuracy and privacy.
-
Representation-Aligned Multi-Scale Personalization for Federated Learning
FRAMP generates client-specific models from compact descriptors in federated learning, trains tailored submodels, and aligns representations to balance personalization with global consistency.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.