𝗔 𝗦𝘂𝗿𝘃𝗲𝘆 𝗼𝗳 𝗜𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺𝘀 𝗳𝗼𝗿 𝗙𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Federated learning lets you train models without moving private data. This protects privacy. But it creates a problem.
Participants must give their data and computing power. This costs them money and energy. Without rewards, people will not join.
This survey examines how to reward participants. It looks at different ways to keep the network running.
Key areas covered:
- Why incentive mechanisms matter for network stability.
- Different types of rewards for data providers.
- How to prevent dishonest participants from cheating.
- Mathematical models used to calculate fair payments.
- Current challenges in scaling these systems.
You need fair rules to make federated learning work at scale. This paper breaks down the math and logic behind those rules.
Read the full survey here: https://dev.to/paperium/a-comprehensive-survey-of-incentive-mechanism-for-federated-learning-9pk
Optional learning community: https://t.me/GyaanSetuAi