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For the communication efficiency issues, the existing study presented new
consensus mechanisms for blockchain-based FEL to reduce communication cost
[4] or developed communication-efficient stochastic gradient descent algorithms
[2], e.g., gradient quantization and encoding [8]. However, the existing schemes
cannot be straightforwardly applied to large-scale FEL because of high communication-
overhead caused by lots of gradients exchanged between edge devices and a cen-
tral server (or miners). The challenges drive the urgent need of developing secure,
decentralized, privacy-preserving and communication-efficient FEL.
To address these challenges, we first propose a Blockchain-empowered Feder-
ated Edge Learning (BFEL) framework without relying on a trusted centralized
server. In BFEL, a consortium blockchain acting as a trusted and decentralized
ledger to manage model updates from edge devices. To filter out malicious or
poisoning model updates, we then propose a Proof-of-Verifying (PoV) consensus
scheme to collaboratively verify the quality of local model updates among pre-
defined miners. Only the verified model updates can be stored into the block for
decentralized federated learning. Since the communication efficiency is signifi-
cantly important for BFEL, we further integrate a gradient compression scheme
into PoV without lowering learning accuracy. This scheme also relieves inference
attack to improve privacy protection of training data.
Moreover, after model training, learning task publishers can share their mod-
els to other entities without enough budget or resources to organize federated
learning. For example, a map company can reuse and trade its traffic-prediction
training model to vehicles for economic benefit. For the sake of security, the
sharing records will be added in the blockchain. However, if both model updates
and model sharing records are stored into a single blockchain, this will result in
larger block size and higher consensus delay. The miners with limited resources
cannot synchronize block data in real time. To avoid this dilemma, we design
further a scalable and flexible framework consisting of a public blockchain as
the main blockchain and multiple consortium blockchains as subchains for per-
formance isolation [9]. Specifically, according to data characteristics and service
demands (e.g., access control), the model updates from edge devices are re-
spectively stored on individual subchains named “Model training subchains”.
Meanwhile, the model sharing records between the task publishers and other
entities are stored in a subchain named “Model trading subchain”.
The main contributions of this paper are summarized as follows.
– Unlike single blockchain-based systems, we design a hierarchical blockchain
framework with a main blockchain and multiple subchains to manage model
updates and model sharing records in a secure, scalable and flexible manner.
– For model training subchains, we design a PoV consensus scheme to filter
out unreliable model updates by allowing miners to collaboratively verify
the quality of model updates for secure BFEL.
– We propose a gradient compression scheme to improve the communication
efficiency of BFEL without compromising learning accuracy, and also to
enhance privacy preservation by mitigating inference attacks.