Decentralized federated learning across edge networks can leverage blockchain with consensus mechanisms for training information exchange among participants over costly and distrustful wide-area networks. However, it is non-trivial to optimally operate the blockchain to support decentralized federated learning due to the complex cost structure of blockchain operations, the balance between blockchain overhead and model convergence, and the dynamics and uncertainties of edge network environments. To overcome these challenges, we formulate a non-linear time-varying integer program that jointly places blockchain nodes and determines the number of training iterations to minimize the long-term blockchain computation and communication cost. We then design an online polynomial-time approximation algorithm that decomposes the problem and solves the subproblems alternately on the fly using only estimated inputs. We rigorously prove the sublinear regret of our approach. We further implement our approach with a prototype system, and conduct extensive trace-driven experiments to validate the superiority of our approach over other alternatives.
Orchestrating Blockchain with Decentralized Federated Learning in Edge Networks
Yibo Jin,Lei Jiao,Zhuzhong Qian,Ruiting Zhou,Lingjun Pu
Published 2023 in Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks
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- Publication year
2023
- Venue
Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks
- Publication date
2023-09-11
- Fields of study
Computer Science, Engineering
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