2021
DOI: 10.1109/tnet.2020.3035770
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Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

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Cited by 280 publications
(184 citation statements)
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“…There are two challenges for solving the optimization problem in (7). On one hand, due to the limited bandwidth, only a small portion of UEs' training loss and model parameters can be updated to AP.…”
Section: Sliding Differential Evolution Based Schedulingmentioning
confidence: 99%
See 3 more Smart Citations
“…There are two challenges for solving the optimization problem in (7). On one hand, due to the limited bandwidth, only a small portion of UEs' training loss and model parameters can be updated to AP.…”
Section: Sliding Differential Evolution Based Schedulingmentioning
confidence: 99%
“…This makes it impossible to calculate the global loss F (w t ) in (7a) accurately. Also, it is difficult to get the relationship between S[t] and w t in (7), where w t relies on model training. On the other hand, this optimization problem is a combinatorial problem which does not normally have low complexity solutions.…”
Section: Sliding Differential Evolution Based Schedulingmentioning
confidence: 99%
See 2 more Smart Citations
“…However, [32] did not consider variability of compute and communication capabilities across clients. Reference [33] proposed FEDL algorithm for allocating radio resources to clients for reducing convergence time. However, we consider the MEC setting with personalized devices where the compute and communication resources of the clients cannot be tuned.…”
Section: Introductionmentioning
confidence: 99%