title: DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
abstract: Federated Learning (FL) is a powerful technique for training a model on a
  server with data from several clients in a privacy-preserving manner. In FL,
  a server sends the model to every client, who then train the model locally
  and send it back to the server. The server aggregates the updated models and
  repeats the process for several rounds. FL incurs significant communication
  costs, in particular when transmitting the updated local models from the
  clients back to the server. Recently proposed algorithms quantize the model
  parameters to efficiently compress FL communication. These algorithms
  typically have a quantization level that controls the compression factor. We
  find that dynamic adaptations of the quantization level can boost
  compression without sacrificing model quality. First, we introduce a
  time-adaptive quantization algorithm that increases the quantization level
  as training progresses. Second, we introduce a client-adaptive quantization
  algorithm that assigns each individual client the optimal quantization level
  at every round. Finally, we combine both algorithms into DAdaQuant, the
  doubly-adaptive quantization algorithm. Our experiments show that DAdaQuant
  consistently improves client$\rightarrow$server compression, outperforming
  the strongest non-adaptive baselines by up to $2.8\times$.
authors: Robert Hönig Yiren Zhao Robert D. Mullins
hIndex: Robert D. Mullins 18