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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Federated Optimization of ℓ0-norm Regularized Sparse Learning
Regularized sparse learning with the ℓ0-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their capability of recovering true support and scalability with large datasets. The current theoretical analysis of IHT assumes the use of centralized IID data. In realistic large-scale scenarios, however, data are distributed, seldom IID, and private to edge computing devices at the local level. Consequently, it is required to study the property of IHT in a federated environment, where local devices update the sparse model individually and communicate with a central server for aggregation infrequently without sharing local data. In this paper, we propose the first group of federated IHT methods: Federated Hard Thresholding (Fed-HT) and Federated Iterative Hard Thresholding (FedIter-HT) with theoretical guarantees. We prove that both algorithms have a linear convergence rate and guarantee for recovering the optimal sparse estimator, which is comparable to classic IHT methods, but with decentralized, non-IID, and unbalanced data. Empirical results demonstrate that the Fed-HT and FedIter-HT outperform their competitor—a distributed IHT, in terms of reducing objective values with fewer communication rounds and bandwidth requirements.
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- Award ID(s):
- 1718738
- NSF-PAR ID:
- 10378100
- Date Published:
- Journal Name:
- Algorithms
- Volume:
- 15
- Issue:
- 9
- ISSN:
- 1999-4893
- Page Range / eLocation ID:
- 319
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/a15090319
