A central theme in federated learning (FL) is the fact that client data distributions are often not independent and identically distributed (IID), which has strong implications on the training process. While most existing FL algorithms focus on the conventional non-IID setting of class imbalance or missing classes across clients, in practice, the distribution differences could be more complex, e.g., changes in class conditional (domain) distributions. In this paper, we consider this complex case in FL wherein each client has access to only one domain distribution. For tasks such as domain generalization, most existing learning algorithms require access to data from multiple clients (i.e., from multiple domains) during training, which is prohibitive in FL. To address this challenge, we propose a federated domain translation method that generates pseudodata for each client which could be useful for multiple downstream learning tasks. We empirically demonstrate that our translation model is more resource-efficient (in terms of both communication and computation) and easier to train in an FL setting than standard domain translation methods. Furthermore, we demonstrate that the learned translation model enables use of state-of-the-art domain generalization methods in a federated setting, which enhances accuracy and robustness to increases in the synchronization period compared to existing methodology.
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This content will become publicly available on July 23, 2024
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Due to the often limited communication bandwidth
of edge devices, most existing federated
learning (FL) methods randomly select only a
subset of devices to participate in training at each
communication round. Compared with engaging
all the available clients, such a random-selection
mechanism could lead to significant performance
degradation on non-IID (independent and identically
distributed) data. In this paper, we present
our key observation that the essential reason resulting
in such performance degradation is the
class-imbalance of the grouped data from randomly
selected clients. Based on this observation,
we design an efficient heterogeneity-aware
client sampling mechanism, namely, Federated
Class-balanced Sampling (Fed-CBS), which can
effectively reduce class-imbalance of the grouped
dataset from the intentionally selected clients.
We first propose a measure of class-imbalance
which can be derived in a privacy-preserving way.
Based on this measure, we design a computationefficient
client sampling strategy such that the actively
selected clients will generate a more classbalanced
grouped dataset with theoretical guarantees.
Experimental results show that Fed-CBS outperforms
the status quo approaches in terms of test
accuracy and the rate of convergence while achieving
comparable or even better performance than
the ideal setting where all the available clients
participate in the FL training.
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- NSF-PAR ID:
- 10441676
- Date Published:
- Journal Name:
- International Conference on Machine Learning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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