FedAvg
FedAvg (Federated Averaging) is a classical model aggregation algorithm in federated learning. The server distributes a global model to clients for local training, then generates a new global model by computing a weighted average of model weights proportional to each participant's sample size. It is the foundational aggregation strategy in federated learning.
Core Facts
Timeline (last 90 days)
The FedAvg algorithm was proposed by McMahan et al. in 2017.
The standard federated aggregation algorithm FedAvg assumes that client gradients can be directly averaged to estimate the global gradient, but simple averaging under Non-IID conditions causes gradient drift.
The basic process of federated learning involves a central server distributing a global model, local nodes training on local data and uploading only gradients or model weight differences, and the server aggregating the updates via algorithms such as FedAvg before distributing a new global model.
In the FedAvg (Federated Averaging) algorithm, the server distributes the global model to each client, each party completes several rounds of gradient updates locally and then uploads model weights or gradients, and the server generates a new global model by taking a weighted average according to sample size.
All Facts (5)
Federated learning allows models to complete training updates locally on the data, uploading only gradients rather than raw data
85%UnverifiedThe FedAvg algorithm was proposed by McMahan et al. in 2017.
50%UnverifiedThe standard federated aggregation algorithm FedAvg assumes that client gradients can be directly averaged to estimate the global gradient, but simple averaging under Non-IID conditions causes gradient drift.
50%UnverifiedThe basic process of federated learning involves a central server distributing a global model, local nodes training on local data and uploading only gradients or model weight differences, and the server aggregating the updates via algorithms such as FedAvg before distributing a new global model.
50%UnverifiedIn the FedAvg (Federated Averaging) algorithm, the server distributes the global model to each client, each party completes several rounds of gradient updates locally and then uploads model weights or gradients, and the server generates a new global model by taking a weighted average according to sample size.
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