Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
A randomized incremental subgradient method for distributed optimization in networked systems,
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Introduces Pac-Man attack on random walks in distributed learning and Average Crossing duplication to ensure survival and convergence of SGD.
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Decentralized Learning via Random Walk with Jumps
Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
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Random Walk Learning and the Pac-Man Attack
Introduces Pac-Man attack on random walks in distributed learning and Average Crossing duplication to ensure survival and convergence of SGD.