The conjunction of edge intelligence and the ever-growing Internet-of-Things
(IoT) network heralds a new era of collaborative machine learning, with
federated learning (FL) emerging as the most prominent paradigm. With the
growing interest in these learning schemes, researchers started addressing some
of their most fundamental limitations. Indeed, conventional FL with a central
aggregator presents a single point of failure and a network bottleneck. To
bypass this issue, decentralized FL where nodes collaborate in a peer-to-peer
network has been proposed. Despite the latter's efficiency, communication costs
and data heterogeneity remain key challenges in decentralized FL. In this
context, we propose a novel scheme, called opportunistic
communication-efficient decentralized federated learning, a.k.a., OCD-FL,
consisting of a systematic FL peer selection for collaboration, aiming to
achieve maximum FL knowledge gain while reducing energy consumption.
Experimental results demonstrate the capability of OCD-FL to achieve similar or
better performances than the fully collaborative FL, while significantly
reducing consumed energy by at least 30% and up to 80%.

通过系统性的 FL 节点协作选择，同时减少能源消耗，使得 OCD-FL 能够在减少至少 30% 且高达 80% 的能源消耗的情况下，实现与全面协作 FL 类似或更好的性能。

OCD-FL: 一种基于对等选择的高效通信的分布式联邦学习

OCD-FL: A Novel Communication-Efficient Peer Selection-based  Decentralized Federated Learning

Federated learning (FL) is a privacy-preserving collaborative learning
framework, and differential privacy can be applied to further enhance its
privacy protection. Existing FL systems typically adopt Federated Average
(FedAvg) as the training algorithm and implement differential privacy with a
Gaussian mechanism. However, the inherent privacy-utility trade-off in these
systems severely degrades the training performance if a tight privacy budget is
enforced. Besides, the Gaussian mechanism requires model weights to be of
high-precision. To improve communication efficiency and achieve a better
privacy-utility trade-off, we propose a communication-efficient FL training
algorithm with differential privacy guarantee. Specifically, we propose to
adopt binary neural networks (BNNs) and introduce discrete noise in the FL
setting. Binary model parameters are uploaded for higher communication
efficiency and discrete noise is added to achieve the client-level differential
privacy protection. The achieved performance guarantee is rigorously proved,
and it is shown to depend on the level of discrete noise. Experimental results
based on MNIST and Fashion-MNIST datasets will demonstrate that the proposed
training algorithm achieves client-level privacy protection with performance
gain while enjoying the benefits of low communication overhead from binary
model updates.

提出了一种具有差分隐私保证的通信高效的联邦学习训练算法，采用二进制神经网络 (BNNs) 并引入离散噪声以实现客户级别的隐私保护。通过实验证明，该算法在保证隐私的同时，实现了较低的通信开销和性能提升。