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WARNING : DEPRECATED FEATURE: flwr.server.start_server() is deprecated.
Instead, use the `flower-superlink` CLI command to start a SuperLink as shown below:
$ flower-superlink --insecure
To view usage and all available options, run:
$ flower-superlink --help
Using `start_server()` is deprecated.
This is a deprecated feature. It will be removed
entirely in future versions of Flower.
INFO : Starting Flower server, config: num_rounds=10, no round_timeout
INFO : Flower ECE: gRPC server running (10 rounds), SSL is disabled
INFO : [INIT]
INFO : Using initial global parameters provided by strategy
INFO : Starting evaluation of initial global parameters
INFO : Evaluation returned no results (`None`)
INFO :
INFO : [ROUND 1]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 2]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 3]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 4]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 5]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 6]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 7]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 8]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 9]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [ROUND 10]
INFO : configure_fit: strategy sampled 3 clients (out of 3)
INFO : aggregate_fit: received 3 results and 0 failures
INFO : configure_evaluate: strategy sampled 3 clients (out of 3)
INFO : aggregate_evaluate: received 3 results and 0 failures
INFO :
INFO : [SUMMARY]
INFO : Run finished 10 round(s) in 146.09s
INFO : History (loss, distributed):
INFO : round 1: 9.925077960899166
INFO : round 2: 1.3836319349268043
INFO : round 3: 0.29234505289901225
INFO : round 4: 0.49308163151779894
INFO : round 5: 0.0446630789378426
INFO : round 6: 0.284914885456677
INFO : round 7: 0.058860671314457845
INFO : round 8: 0.07942846971222137
INFO : round 9: 0.037627383500482725
INFO : round 10: 0.024166644675821698
INFO : History (metrics, distributed, fit):
INFO : {'client_1_train_loss': [(1, 0.011821450406897624),
INFO : (2, 9.19087529878669),
INFO : (3, 0.6059146807741652),
INFO : (4, 0.19704961071376878),
INFO : (5, 0.13989461602253406),
INFO : (6, 0.020903305802541017),
INFO : (7, 0.1471804918425052),
INFO : (8, 0.03622452800638312),
INFO : (9, 0.021778890335108998),
INFO : (10, 0.014076102254991698)],
INFO : 'client_2_train_loss': [(1, 0.005994700957002613),
INFO : (2, 9.031767151078034),
INFO : (3, 0.7539265559325762),
INFO : (4, 0.16390307652051578),
INFO : (5, 0.20438744467001307),
INFO : (6, 0.016729887813302254),
INFO : (7, 0.12536963731445314),
INFO : (8, 0.03203308185613172),
INFO : (9, 0.01797729913919752),
INFO : (10, 0.014847784393701121)],
INFO : 'client_3_train_loss': [(1, 0.0477309877821325),
INFO : (2, 8.576512244819956),
INFO : (3, 0.7021478945708147),
INFO : (4, 0.20970996846132312),
INFO : (5, 0.2520780406049325),
INFO : (6, 0.039224337560547065),
INFO : (7, 0.19049448489231752),
INFO : (8, 0.03907830891323567),
INFO : (9, 0.03410547275085612),
INFO : (10, 0.03132038051823111)],
INFO : 'global_train_loss': [(1, 0.024723581809669777),
INFO : (2, 8.885175558524038),
INFO : (3, 0.6942090508692439),
INFO : (4, 0.19139092719798328),
INFO : (5, 0.20735043383995075),
INFO : (6, 0.027091810488224646),
INFO : (7, 0.15788991120164078),
INFO : (8, 0.03602586002639032),
INFO : (9, 0.025616361683664917),
INFO : (10, 0.021443837521512663)]}
INFO : History (metrics, distributed, evaluate):
INFO : {'client_1_eval_loss': [(1, 9.976542702888077),
INFO : (2, 1.2864945372182275),
INFO : (3, 0.2793154488706755),
INFO : (4, 0.5965077027088073),
INFO : (5, 0.03825337379663002),
INFO : (6, 0.2677974948383068),
INFO : (7, 0.058361862074286265),
INFO : (8, 0.08157266843303032),
INFO : (9, 0.029206974828750388),
INFO : (10, 0.02422947784015051)],
INFO : 'client_2_eval_loss': [(1, 10.00263674723258),
INFO : (2, 1.3619409297631837),
INFO : (3, 0.24040741897930812),
INFO : (4, 0.38867445768531034),
INFO : (5, 0.032692949841941314),
INFO : (6, 0.2738929928920669),
INFO : (7, 0.060165285674857814),
INFO : (8, 0.07285282868754381),
INFO : (9, 0.029050754143804952),
INFO : (10, 0.01716736676326117)],
INFO : 'client_3_eval_loss': [(1, 9.831531776215563),
INFO : (2, 1.461328144772189),
INFO : (3, 0.3416395899531878),
INFO : (4, 0.5114476139352474),
INFO : (5, 0.05814581121184792),
INFO : (6, 0.3043146318768071),
INFO : (7, 0.058136951014934914),
INFO : (8, 0.08330663724478181),
INFO : (9, 0.049671815384144585),
INFO : (10, 0.029676996694472633)],
INFO : 'global_eval_loss': [(1, 9.925078071091885),
INFO : (2, 1.3836319280429141),
INFO : (3, 0.29234505846417363),
INFO : (4, 0.4930816239130249),
INFO : (5, 0.0446630791944702),
INFO : (6, 0.2849148775837404),
INFO : (7, 0.058860671175644294),
INFO : (8, 0.07942846919600849),
INFO : (9, 0.03762738416111641),
INFO : (10, 0.024166644736454038)]}
INFO :
Tamanho da Previs�o: 90
============================================================
SERVIDOR DE APRENDIZADO FEDERADO
============================================================
Estrat�gia: FEDADAGRAD
Rodadas: 10
Clientes m�nimos: 3
============================================================
Iniciando servidor FL...
============================================================
TREINAMENTO CONCLU�DO - GERANDO AN�LISES
============================================================
Visualiza��es salvas em results
M�tricas detalhadas salvas em results\detailed_metrics_fedadagrad.csv
An�lise estat�stica salva em results\analysis_fedadagrad.json
============================================================
RESUMO DO TREINAMENTO
============================================================
Perda inicial de treino: 0.024724
Perda final de treino: 0.021444
Melhoria no treino: 13.27%
Perda inicial de valida��o: 9.925078
Perda final de valida��o: 0.024167
Melhoria na valida��o: 99.76%
Desvio padr�o final entre clientes: 0.005121
Converg�ncia: Boa
============================================================
Resultados salvos em: results
============================================================