TY - GEN
T1 - An Introduction to Gossip Protocol Based Learning in Peer-to-Peer Federated Learning
AU - Naik, Dishita
AU - Grace, Paul
AU - Naik, Nitin
AU - Jenkins, Paul
AU - Mishra, Durgesh
AU - Prajapat, Shaligram
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/12/8
Y1 - 2023/12/8
N2 - Machine learning (ML) has been progressively implemented in a distributed manner to harness the data abundance produced on billions of end user devices. Federated learning (FL) is a type of distributed machine learning that provides robust data privacy by training ML models locally on each participating node without directly exchanging raw data with others. The local ML model updates from all nodes are aggregated in order to obtain a global model. There are different ways to aggregate local model updates for obtaining a global model, such as centralized and decentralized/peer-to-peer. Centralized FL (CFL) requires a server to collect and aggregate all model updates for producing a global model, whereas decentralized FL (DFL)/peer-to-peer FL (P2PFL) requires coordination among all the nodes to communicate and aggregate all models updates and produce a global model. In CFL, the server can pose a bottleneck problem as it is a single point of failure, though, this issue can be resolved using DFL/P2PFL. However, the design of the decentralized/peer-to-peer architecture is complex and challenging, and may incur significant communication overhead due to a large number of nodes involved in the learning process. Gossip protocols are one of the most effective ways to communicate in DFL/P2PFL and optimises its performance, therefore, this decentralized learning is also known as gossip protocol-based learning. Considering the importance of gossip protocol in DFL/P2PFL, this paper will analyse gossip protocol, working of gossip protocol, types of gossip protocol, benefits and limitations of gossip protocol, usages of gossip protocol in DFL/P2PFL and its two main types structured P2PFL and unstructured P2PFL.
AB - Machine learning (ML) has been progressively implemented in a distributed manner to harness the data abundance produced on billions of end user devices. Federated learning (FL) is a type of distributed machine learning that provides robust data privacy by training ML models locally on each participating node without directly exchanging raw data with others. The local ML model updates from all nodes are aggregated in order to obtain a global model. There are different ways to aggregate local model updates for obtaining a global model, such as centralized and decentralized/peer-to-peer. Centralized FL (CFL) requires a server to collect and aggregate all model updates for producing a global model, whereas decentralized FL (DFL)/peer-to-peer FL (P2PFL) requires coordination among all the nodes to communicate and aggregate all models updates and produce a global model. In CFL, the server can pose a bottleneck problem as it is a single point of failure, though, this issue can be resolved using DFL/P2PFL. However, the design of the decentralized/peer-to-peer architecture is complex and challenging, and may incur significant communication overhead due to a large number of nodes involved in the learning process. Gossip protocols are one of the most effective ways to communicate in DFL/P2PFL and optimises its performance, therefore, this decentralized learning is also known as gossip protocol-based learning. Considering the importance of gossip protocol in DFL/P2PFL, this paper will analyse gossip protocol, working of gossip protocol, types of gossip protocol, benefits and limitations of gossip protocol, usages of gossip protocol in DFL/P2PFL and its two main types structured P2PFL and unstructured P2PFL.
KW - Centralized Federated Learning
KW - Decentralized Federated Learning
KW - Distributed Machine Learning
KW - DML
KW - Epidemic Protocol
KW - Federated Learning
KW - Federated Machine Learning
KW - FL
KW - FML
KW - Gossip Protocol
KW - Gossip Protocol Based Learning
KW - Peer-to-Peer Federated Learning
KW - Structured P2PFL
KW - Unstructured P2PFL
UR - http://www.scopus.com/inward/record.url?scp=85189239189&partnerID=8YFLogxK
U2 - 10.1109/ictbig59752.2023.10456324
DO - 10.1109/ictbig59752.2023.10456324
M3 - Conference contribution
T3 - 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
BT - 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Y2 - 8 December 2023 through 9 December 2023
ER -