CMFL: Mitigating communication overhead for federated learning
… Assumptions: Our convergence analysis is based on two assumptions. First, we assume …
of Federated Learning while at the same time providing guaranteed learning convergence. Our …
of Federated Learning while at the same time providing guaranteed learning convergence. Our …
Convergence and accuracy trade-offs in federated learning and meta-learning
… There are strong connections between federated learning and meta-learning, despite
differences in practical concerns. Formal connections between the two were shown by Khodak et al…
differences in practical concerns. Formal connections between the two were shown by Khodak et al…
Flexible vertical federated learning with heterogeneous parties
… We provide theoretical convergence analysis for Flex-VFL and show that the convergence
… We apply this analysis and extend our algorithm to adapt party learning rates in response to …
… We apply this analysis and extend our algorithm to adapt party learning rates in response to …
Communication-efficient federated learning
… Finally, we analyze the convergence of our proposed FL framework. Simulation results
based on real-world data demonstrate the performance of our proposed FL framework and its …
based on real-world data demonstrate the performance of our proposed FL framework and its …
Fedcluster: Boosting the convergence of federated learning via cluster-cycling
… federated learning framework with improved optimization efficiency, and investigate its
theoretical convergence … We provide theoretical convergence analysis to show that FedCluster …
theoretical convergence … We provide theoretical convergence analysis to show that FedCluster …
Fedpd: A federated learning framework with adaptivity to non-iid data
… CONVERGENCE & COMPLEXITY ANALYSIS In this section, we first provide a basic
convergence analysis of FedPD without assuming A3 (or effectively, with G in (3) being infinity). …
convergence analysis of FedPD without assuming A3 (or effectively, with G in (3) being infinity). …
CFedAvg: achieving efficient communication and fast convergence in non-iid federated learning
… The first two assumptions and the bounded local variance assumption in Assumption 3 are
standard assumptions in the convergence analysis of stochastic gradient-type algorithms in …
standard assumptions in the convergence analysis of stochastic gradient-type algorithms in …
On the convergence of decentralized federated learning under imperfect information sharing
VP Chellapandi, A Upadhyay… - IEEE Control Systems …, 2023 - ieeexplore.ieee.org
… We use the above assumption in the convergence analysis of FedNDL3. Theoretically
speaking Assumption 6 can be viewed as a general formulation of the recursive upperbound on …
speaking Assumption 6 can be viewed as a general formulation of the recursive upperbound on …
Mobility accelerates learning: Convergence analysis on hierarchical federated learning in vehicular networks
… Abstract—Hierarchical federated learning (HFL) enables distributed training of models …
Through convergence analysis, we show that mobility influences the convergence speed by both …
Through convergence analysis, we show that mobility influences the convergence speed by both …
Federated learning with cooperating devices: A consensus approach for massive IoT networks
Federated learning (FL) is emerging as a new paradigm to train machine learning (ML)
models in distributed systems. Rather than sharing and disclosing the training data set with the …
models in distributed systems. Rather than sharing and disclosing the training data set with the …