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Found 8 papers, 1 papers with code
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy.
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients.
DAGC: Data-Volume-Aware Adaptive Sparsification Gradient Compression for Distributed Machine Learning in Mobile Computing
Assigning varying compression ratios to workers with distinct data distributions and volumes is thus a promising solution.
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources
To boost the convergence of DFL, a vehicle tunes the aggregation weight of each data source by minimizing the KL divergence of its state vector, and its effectiveness in diversifying data sources can be theoretically proved.
A Fast Blockchain-based Federated Learning Framework with Compressed Communications
Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL).
Magic ELF: Image Deraining Meets Association Learning and Transformer
Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications.
Optimal Rate Adaption in Federated Learning with Compressed Communications
Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates.
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
Then, we further propose the boosted MUCSC (B-MUCSC) algorithm, a biased compression algorithm that can achieve an extremely high compression rate by grouping insignificant model updates into a super cluster.
